{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Oute_TTS_(1B).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CADH_fo_U0SB"
   },
   "source": [
    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
    "<div class=\"align-center\">\n",
    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
    "</div>\n",
    "\n",
    "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n",
    "\n",
    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3NPPXizTU0SD"
   },
   "source": [
    "### News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCDFTFmBU0SD"
   },
   "source": [
    "Read our **[TTS Guide](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning)** for instructions and all our notebooks.\n",
    "\n",
    "Read our **[Qwen3 Guide](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** and check out our new **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants which outperforms other quantization methods!\n",
    "\n",
    "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tJKbAJ9pU0SE"
   },
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "2JxnTqWNU0SE"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "import os\n",
    "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
    "    !pip install unsloth\n",
    "else:\n",
    "    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
    "    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo\n",
    "    !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n",
    "    !pip install --no-deps unsloth\n",
    "!pip install omegaconf einx\n",
    "!rm -rf OuteTTS && git clone https://github.com/edwko/OuteTTS\n",
    "import os\n",
    "os.remove(\"/content/OuteTTS/outetts/models/gguf_model.py\")\n",
    "os.remove(\"/content/OuteTTS/outetts/interface.py\")\n",
    "os.remove(\"/content/OuteTTS/outetts/__init__.py\")\n",
    "!pip install pyloudnorm openai-whisper uroman MeCab loguru flatten_dict ffmpy randomname argbind tiktoken ftfy\n",
    "!pip install descript-audio-codec descript-audiotools julius openai-whisper --no-deps\n",
    "%env UNSLOTH_DISABLE_FAST_GENERATION = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AkWYsztAs9Ky"
   },
   "source": [
    "### Unsloth\n",
    "\n",
    "`FastModel` supports loading nearly any model now! This includes Vision and Text models!\n",
    "\n",
    "Thank you to [Etherl](https://huggingface.co/Etherll) for creating this notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QmUBVEnvCDJv",
    "outputId": "d2651d0c-6e13-4f41-ffec-50a90ec962d3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
      "==((====))==  Unsloth 2025.5.7: Fast Llama patching. Transformers: 4.51.3.\n",
      "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
      "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n",
      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
      "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n"
     ]
    }
   ],
   "source": [
    "from unsloth import FastModel\n",
    "import torch\n",
    "max_seq_length = 2048 # Choose any for long context!\n",
    "fourbit_models = [\n",
    "    # 4bit dynamic quants for superior accuracy and low memory use\n",
    "    \"unsloth/gemma-3-4b-it-unsloth-bnb-4bit\",\n",
    "    \"unsloth/gemma-3-12b-it-unsloth-bnb-4bit\",\n",
    "    \"unsloth/gemma-3-27b-it-unsloth-bnb-4bit\",\n",
    "    # Qwen3 new models\n",
    "    \"unsloth/Qwen3-4B-unsloth-bnb-4bit\",\n",
    "    \"unsloth/Qwen3-8B-unsloth-bnb-4bit\",\n",
    "    # Other very popular models!\n",
    "    \"unsloth/Llama-3.1-8B\",\n",
    "    \"unsloth/Llama-3.2-3B\",\n",
    "    \"unsloth/Llama-3.3-70B\",\n",
    "    \"unsloth/mistral-7b-instruct-v0.3\",\n",
    "    \"unsloth/Phi-4\",\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model, tokenizer = FastModel.from_pretrained(\n",
    "    model_name = \"unsloth/Llama-OuteTTS-1.0-1B\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dtype = None, # Set to None for auto detection\n",
    "    load_in_4bit = False, # Set to True for 4bit which reduces memory\n",
    "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXd9bTZd1aaL"
   },
   "source": [
    "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "95059308-9e48-4a29-e171-146ed175b944"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Unsloth: Making `model.base_model.model.model` require gradients\n"
     ]
    }
   ],
   "source": [
    "model = FastModel.get_peft_model(\n",
    "    model,\n",
    "    r = 128, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
    "    target_modules = [\"q_proj\", \"v_proj\",],\n",
    "    lora_alpha = 128,\n",
    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
    "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vITh0KVJ10qX"
   },
   "source": [
    "<a name=\"Data\"></a>\n",
    "### Data Prep  \n",
    "\n",
    "We will use the `MrDragonFox/Elise`, which is designed for training TTS models. Ensure that your dataset follows the required format: **text, audio**, but maintaining the correct structure is essential for optimal training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "LjY75GoYUCB8"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset,Audio,Dataset\n",
    "dataset = load_dataset(\"MrDragonFox/Elise\", split = \"train\")\n",
    "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=24000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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      "52cad4bbdedc4d55a391b3d4a76237df",
      "959270534e614bbba51f1e2d968473c3"
     ]
    },
    "id": "zK94B-Pfioto",
    "outputId": "ede6a777-6944-4550-edc7-b90a91fc54c3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Using device: cuda\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "weights_24khz_1.5kbps_v1.0.pth:   0%|          | 0.00/296M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a6834d987f2b45c0ad1cb3b7391c0cbd"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
      "  WeightNorm.apply(module, name, dim)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/1.28M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "58b884585d514bc386006389af5f2b7d"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/18.4M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0fd5f926f9fa4ac9a399b2afca11aa9c"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/131k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "03fd8e39f74a4bc1b9fa82c60377e0d0"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Loading Whisper model: turbo on cuda\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████| 1.51G/1.51G [00:18<00:00, 87.1MiB/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Whisper model loaded.\n",
      "Starting dataset processing...\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\rProcessing Dataset:   0%|          | 0/1195 [00:00<?, ?it/s]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "  0%|          | 0/3 [00:00<?, ?it/s]\u001b[A\n",
      " 33%|███▎      | 1/3 [00:01<00:03,  1.64s/it]\u001b[A\n",
      "100%|██████████| 3/3 [00:01<00:00,  1.54it/s]\n",
      "Processing Dataset:   0%|          | 1/1195 [00:32<10:45:38, 32.44s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 94.61it/s]\n",
      "Processing Dataset:   0%|          | 2/1195 [00:33<4:41:51, 14.18s/it] /usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.31it/s]\n",
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      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
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      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 35.75it/s]\n",
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      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
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      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
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      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
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      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
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      "100%|██████████| 1/1 [00:00<00:00, 34.72it/s]\n",
      "Processing Dataset:   2%|▏         | 22/1195 [01:02<24:15,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 92.65it/s]\n",
      "Processing Dataset:   2%|▏         | 23/1195 [01:03<24:21,  1.25s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 93.25it/s]\n",
      "Processing Dataset:   2%|▏         | 24/1195 [01:04<23:57,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 37.11it/s]\n",
      "Processing Dataset:   2%|▏         | 25/1195 [01:06<24:35,  1.26s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 100.90it/s]\n",
      "Processing Dataset:   2%|▏         | 26/1195 [01:07<24:54,  1.28s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 95.90it/s]\n",
      "Processing Dataset:   2%|▏         | 27/1195 [01:08<23:07,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.52it/s]\n",
      "Processing Dataset:   2%|▏         | 28/1195 [01:09<22:48,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.62it/s]\n",
      "Processing Dataset:   2%|▏         | 29/1195 [01:10<22:37,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.32it/s]\n",
      "Processing Dataset:   3%|▎         | 30/1195 [01:11<22:02,  1.14s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 95.75it/s]\n",
      "Processing Dataset:   3%|▎         | 31/1195 [01:13<23:10,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.71it/s]\n",
      "Processing Dataset:   3%|▎         | 32/1195 [01:14<23:36,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.77it/s]\n",
      "Processing Dataset:   3%|▎         | 33/1195 [01:15<22:46,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.09it/s]\n",
      "Processing Dataset:   3%|▎         | 34/1195 [01:16<22:38,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 34.26it/s]\n",
      "Processing Dataset:   3%|▎         | 35/1195 [01:17<22:38,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 20.94it/s]\n",
      "Processing Dataset:   3%|▎         | 36/1195 [01:19<22:15,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.32it/s]\n",
      "Processing Dataset:   3%|▎         | 37/1195 [01:20<23:25,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.54it/s]\n",
      "Processing Dataset:   3%|▎         | 38/1195 [01:21<22:40,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.05it/s]\n",
      "Processing Dataset:   3%|▎         | 39/1195 [01:22<22:10,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.35it/s]\n",
      "Processing Dataset:   3%|▎         | 40/1195 [01:23<23:19,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.29it/s]\n",
      "Processing Dataset:   3%|▎         | 41/1195 [01:25<23:29,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.13it/s]\n",
      "Processing Dataset:   4%|▎         | 42/1195 [01:26<23:01,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 91.45it/s]\n",
      "Processing Dataset:   4%|▎         | 43/1195 [01:27<22:53,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.41it/s]\n",
      "Processing Dataset:   4%|▎         | 44/1195 [01:28<23:14,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 29.71it/s]\n",
      "Processing Dataset:   4%|▍         | 45/1195 [01:29<22:49,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.36it/s]\n",
      "Processing Dataset:   4%|▍         | 46/1195 [01:31<24:07,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.98it/s]\n",
      "Processing Dataset:   4%|▍         | 47/1195 [01:32<25:41,  1.34s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.05it/s]\n",
      "Processing Dataset:   4%|▍         | 48/1195 [01:33<24:16,  1.27s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 97.62it/s]\n",
      "Processing Dataset:   4%|▍         | 49/1195 [01:35<23:43,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 93.62it/s]\n",
      "Processing Dataset:   4%|▍         | 50/1195 [01:36<23:53,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 99.34it/s]\n",
      "Processing Dataset:   4%|▍         | 51/1195 [01:37<24:11,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.62it/s]\n",
      "Processing Dataset:   4%|▍         | 52/1195 [01:38<23:01,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.25it/s]\n",
      "Processing Dataset:   4%|▍         | 53/1195 [01:39<22:16,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.23it/s]\n",
      "Processing Dataset:   5%|▍         | 54/1195 [01:40<21:47,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 34.14it/s]\n",
      "Processing Dataset:   5%|▍         | 55/1195 [01:42<21:32,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 71.75it/s]\n",
      "Processing Dataset:   5%|▍         | 56/1195 [01:43<22:31,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.88it/s]\n",
      "Processing Dataset:   5%|▍         | 57/1195 [01:44<24:06,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 93.57it/s]\n",
      "Processing Dataset:   5%|▍         | 58/1195 [01:45<22:45,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.76it/s]\n",
      "Processing Dataset:   5%|▍         | 59/1195 [01:47<24:16,  1.28s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 32.71it/s]\n",
      "Processing Dataset:   5%|▌         | 60/1195 [01:48<23:11,  1.23s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 33.88it/s]\n",
      "Processing Dataset:   5%|▌         | 61/1195 [01:49<21:53,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.68it/s]\n",
      "Processing Dataset:   5%|▌         | 62/1195 [01:50<22:11,  1.18s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 31.32it/s]\n",
      "Processing Dataset:   5%|▌         | 63/1195 [01:51<20:41,  1.10s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 34.52it/s]\n",
      "Processing Dataset:   5%|▌         | 64/1195 [01:55<37:54,  2.01s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.29it/s]\n",
      "Processing Dataset:   5%|▌         | 65/1195 [01:57<34:03,  1.81s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 88.93it/s]\n",
      "Processing Dataset:   6%|▌         | 66/1195 [01:58<30:14,  1.61s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 80.76it/s]\n",
      "Processing Dataset:   6%|▌         | 67/1195 [01:59<27:05,  1.44s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 89.81it/s]\n",
      "Processing Dataset:   6%|▌         | 68/1195 [02:00<25:56,  1.38s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 83.58it/s]\n",
      "Processing Dataset:   6%|▌         | 69/1195 [02:01<23:31,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.39it/s]\n",
      "Processing Dataset:   6%|▌         | 70/1195 [02:02<22:03,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.75it/s]\n",
      "Processing Dataset:   6%|▌         | 71/1195 [02:03<22:49,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.88it/s]\n",
      "Processing Dataset:   6%|▌         | 72/1195 [02:04<22:23,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 54.39it/s]\n",
      "Processing Dataset:   6%|▌         | 73/1195 [02:06<22:12,  1.19s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 21.16it/s]\n",
      "Processing Dataset:   6%|▌         | 74/1195 [02:07<21:08,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.29it/s]\n",
      "Processing Dataset:   6%|▋         | 75/1195 [02:08<21:53,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 94.54it/s]\n",
      "Processing Dataset:   6%|▋         | 76/1195 [02:09<22:32,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.54it/s]\n",
      "Processing Dataset:   6%|▋         | 77/1195 [02:10<21:40,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.71it/s]\n",
      "Processing Dataset:   7%|▋         | 78/1195 [02:11<21:36,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.40it/s]\n",
      "Processing Dataset:   7%|▋         | 79/1195 [02:12<21:25,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.41it/s]\n",
      "Processing Dataset:   7%|▋         | 80/1195 [02:14<21:46,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.42it/s]\n",
      "Processing Dataset:   7%|▋         | 81/1195 [02:15<21:28,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.78it/s]\n",
      "Processing Dataset:   7%|▋         | 82/1195 [02:16<21:33,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.53it/s]\n",
      "Processing Dataset:   7%|▋         | 83/1195 [02:17<22:29,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 32.05it/s]\n",
      "Processing Dataset:   7%|▋         | 84/1195 [02:18<21:53,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 60.12it/s]\n",
      "Processing Dataset:   7%|▋         | 85/1195 [02:20<24:13,  1.31s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.76it/s]\n",
      "Processing Dataset:   7%|▋         | 86/1195 [02:21<22:53,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.29it/s]\n",
      "Processing Dataset:   7%|▋         | 87/1195 [02:22<22:37,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.50it/s]\n",
      "Processing Dataset:   7%|▋         | 88/1195 [02:23<22:07,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.84it/s]\n",
      "Processing Dataset:   7%|▋         | 89/1195 [02:25<22:15,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.20it/s]\n",
      "Processing Dataset:   8%|▊         | 90/1195 [02:26<21:45,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.92it/s]\n",
      "Processing Dataset:   8%|▊         | 91/1195 [02:27<22:26,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.54it/s]\n",
      "Processing Dataset:   8%|▊         | 92/1195 [02:28<21:40,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.98it/s]\n",
      "Processing Dataset:   8%|▊         | 93/1195 [02:29<21:54,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 35.49it/s]\n",
      "Processing Dataset:   8%|▊         | 94/1195 [02:30<21:30,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 65.16it/s]\n",
      "Processing Dataset:   8%|▊         | 95/1195 [02:32<23:03,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.23it/s]\n",
      "Processing Dataset:   8%|▊         | 96/1195 [02:33<22:05,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 87.81it/s]\n",
      "Processing Dataset:   8%|▊         | 97/1195 [02:34<21:47,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 93.02it/s]\n",
      "Processing Dataset:   8%|▊         | 98/1195 [02:35<21:03,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.09it/s]\n",
      "Processing Dataset:   8%|▊         | 99/1195 [02:36<20:40,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.95it/s]\n",
      "Processing Dataset:   8%|▊         | 100/1195 [02:37<20:40,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 97.12it/s]\n",
      "Processing Dataset:   8%|▊         | 101/1195 [02:39<20:50,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 88.69it/s]\n",
      "Processing Dataset:   9%|▊         | 102/1195 [02:40<21:18,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.61it/s]\n",
      "Processing Dataset:   9%|▊         | 103/1195 [02:41<20:59,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.54it/s]\n",
      "Processing Dataset:   9%|▊         | 104/1195 [02:42<19:57,  1.10s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.22it/s]\n",
      "Processing Dataset:   9%|▉         | 105/1195 [02:43<20:15,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.78it/s]\n",
      "Processing Dataset:   9%|▉         | 106/1195 [02:45<21:47,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.91it/s]\n",
      "Processing Dataset:   9%|▉         | 107/1195 [02:46<21:55,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.49it/s]\n",
      "Processing Dataset:   9%|▉         | 108/1195 [02:47<21:02,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 90.47it/s]\n",
      "Processing Dataset:   9%|▉         | 109/1195 [02:48<22:39,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.94it/s]\n",
      "Processing Dataset:   9%|▉         | 110/1195 [02:49<22:03,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.32it/s]\n",
      "Processing Dataset:   9%|▉         | 111/1195 [02:51<21:44,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 86.53it/s]\n",
      "Processing Dataset:   9%|▉         | 112/1195 [02:52<21:15,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.00it/s]\n",
      "Processing Dataset:   9%|▉         | 113/1195 [02:53<21:16,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.88it/s]\n",
      "Processing Dataset:  10%|▉         | 114/1195 [02:54<20:51,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.51it/s]\n",
      "Processing Dataset:  10%|▉         | 115/1195 [02:55<21:47,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.21it/s]\n",
      "Processing Dataset:  10%|▉         | 116/1195 [02:57<23:13,  1.29s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 32.90it/s]\n",
      "Processing Dataset:  10%|▉         | 117/1195 [02:58<22:00,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 87.11it/s]\n",
      "Processing Dataset:  10%|▉         | 118/1195 [02:59<22:39,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.48it/s]\n",
      "Processing Dataset:  10%|▉         | 119/1195 [03:00<22:06,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.99it/s]\n",
      "Processing Dataset:  10%|█         | 120/1195 [03:01<20:57,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 87.97it/s]\n",
      "Processing Dataset:  10%|█         | 121/1195 [03:03<21:00,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.05it/s]\n",
      "Processing Dataset:  10%|█         | 122/1195 [03:04<20:58,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.13it/s]\n",
      "Processing Dataset:  10%|█         | 123/1195 [03:05<20:42,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 94.87it/s]\n",
      "Processing Dataset:  10%|█         | 124/1195 [03:06<21:57,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.73it/s]\n",
      "Processing Dataset:  10%|█         | 125/1195 [03:07<21:15,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 28.36it/s]\n",
      "Processing Dataset:  11%|█         | 126/1195 [03:09<21:43,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.95it/s]\n",
      "Processing Dataset:  11%|█         | 127/1195 [03:10<21:42,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.68it/s]\n",
      "Processing Dataset:  11%|█         | 128/1195 [03:11<20:12,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.67it/s]\n",
      "Processing Dataset:  11%|█         | 129/1195 [03:12<20:07,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.96it/s]\n",
      "Processing Dataset:  11%|█         | 130/1195 [03:13<20:06,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.73it/s]\n",
      "Processing Dataset:  11%|█         | 131/1195 [03:14<19:48,  1.12s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.85it/s]\n",
      "Processing Dataset:  11%|█         | 132/1195 [03:15<20:11,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.56it/s]\n",
      "Processing Dataset:  11%|█         | 133/1195 [03:16<19:38,  1.11s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.61it/s]\n",
      "Processing Dataset:  11%|█         | 134/1195 [03:18<20:28,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 33.39it/s]\n",
      "Processing Dataset:  11%|█▏        | 135/1195 [03:19<21:18,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.79it/s]\n",
      "Processing Dataset:  11%|█▏        | 136/1195 [03:20<21:22,  1.21s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 59.38it/s]\n",
      "Processing Dataset:  11%|█▏        | 137/1195 [03:22<22:25,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.25it/s]\n",
      "Processing Dataset:  12%|█▏        | 138/1195 [03:23<22:17,  1.27s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.37it/s]\n",
      "Processing Dataset:  12%|█▏        | 139/1195 [03:24<22:56,  1.30s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.34it/s]\n",
      "Processing Dataset:  12%|█▏        | 140/1195 [03:25<21:41,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.19it/s]\n",
      "Processing Dataset:  12%|█▏        | 141/1195 [03:26<21:16,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 88.41it/s]\n",
      "Processing Dataset:  12%|█▏        | 142/1195 [03:28<21:14,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.63it/s]\n",
      "Processing Dataset:  12%|█▏        | 143/1195 [03:29<21:11,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.78it/s]\n",
      "Processing Dataset:  12%|█▏        | 144/1195 [03:30<20:36,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 56.24it/s]\n",
      "Processing Dataset:  12%|█▏        | 145/1195 [03:31<20:17,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 64.42it/s]\n",
      "Processing Dataset:  12%|█▏        | 146/1195 [03:33<22:38,  1.30s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 32.07it/s]\n",
      "Processing Dataset:  12%|█▏        | 147/1195 [03:34<21:06,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 94.45it/s]\n",
      "Processing Dataset:  12%|█▏        | 148/1195 [03:35<21:16,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.18it/s]\n",
      "Processing Dataset:  12%|█▏        | 149/1195 [03:36<21:14,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.80it/s]\n",
      "Processing Dataset:  13%|█▎        | 150/1195 [03:38<21:46,  1.25s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 85.67it/s]\n",
      "Processing Dataset:  13%|█▎        | 151/1195 [03:39<20:47,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.51it/s]\n",
      "Processing Dataset:  13%|█▎        | 152/1195 [03:40<20:03,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.36it/s]\n",
      "Processing Dataset:  13%|█▎        | 153/1195 [03:41<20:54,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.42it/s]\n",
      "Processing Dataset:  13%|█▎        | 154/1195 [03:42<21:02,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.87it/s]\n",
      "Processing Dataset:  13%|█▎        | 155/1195 [03:43<19:59,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 27.87it/s]\n",
      "Processing Dataset:  13%|█▎        | 156/1195 [03:44<20:09,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.61it/s]\n",
      "Processing Dataset:  13%|█▎        | 157/1195 [03:46<21:51,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 84.22it/s]\n",
      "Processing Dataset:  13%|█▎        | 158/1195 [03:47<21:03,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.76it/s]\n",
      "Processing Dataset:  13%|█▎        | 159/1195 [03:48<20:48,  1.21s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 32.09it/s]\n",
      "Processing Dataset:  13%|█▎        | 160/1195 [03:49<19:45,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.48it/s]\n",
      "Processing Dataset:  13%|█▎        | 161/1195 [03:50<19:44,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.31it/s]\n",
      "Processing Dataset:  14%|█▎        | 162/1195 [03:51<19:45,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.89it/s]\n",
      "Processing Dataset:  14%|█▎        | 163/1195 [03:53<20:36,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.79it/s]\n",
      "Processing Dataset:  14%|█▎        | 164/1195 [03:54<21:25,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 38.44it/s]\n",
      "Processing Dataset:  14%|█▍        | 165/1195 [03:55<21:14,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.59it/s]\n",
      "Processing Dataset:  14%|█▍        | 166/1195 [03:57<22:17,  1.30s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.58it/s]\n",
      "Processing Dataset:  14%|█▍        | 167/1195 [03:58<22:26,  1.31s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 32.71it/s]\n",
      "Processing Dataset:  14%|█▍        | 168/1195 [03:59<20:51,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.68it/s]\n",
      "Processing Dataset:  14%|█▍        | 169/1195 [04:00<20:31,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 89.65it/s]\n",
      "Processing Dataset:  14%|█▍        | 170/1195 [04:02<20:54,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.81it/s]\n",
      "Processing Dataset:  14%|█▍        | 171/1195 [04:03<21:02,  1.23s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.91it/s]\n",
      "Processing Dataset:  14%|█▍        | 172/1195 [04:04<21:16,  1.25s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 27.69it/s]\n",
      "Processing Dataset:  14%|█▍        | 173/1195 [04:05<20:09,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 89.30it/s]\n",
      "Processing Dataset:  15%|█▍        | 174/1195 [04:06<20:13,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.22it/s]\n",
      "Processing Dataset:  15%|█▍        | 175/1195 [04:07<19:10,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 30.18it/s]\n",
      "Processing Dataset:  15%|█▍        | 176/1195 [04:09<19:15,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.21it/s]\n",
      "Processing Dataset:  15%|█▍        | 177/1195 [04:10<20:59,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.75it/s]\n",
      "Processing Dataset:  15%|█▍        | 178/1195 [04:11<21:22,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.97it/s]\n",
      "Processing Dataset:  15%|█▍        | 179/1195 [04:13<21:26,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.70it/s]\n",
      "Processing Dataset:  15%|█▌        | 180/1195 [04:14<20:33,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.01it/s]\n",
      "Processing Dataset:  15%|█▌        | 181/1195 [04:15<20:35,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.69it/s]\n",
      "Processing Dataset:  15%|█▌        | 182/1195 [04:16<20:12,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.50it/s]\n",
      "Processing Dataset:  15%|█▌        | 183/1195 [04:17<20:51,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.56it/s]\n",
      "Processing Dataset:  15%|█▌        | 184/1195 [04:18<20:02,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.79it/s]\n",
      "Processing Dataset:  15%|█▌        | 185/1195 [04:20<20:39,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 33.35it/s]\n",
      "Processing Dataset:  16%|█▌        | 186/1195 [04:21<21:39,  1.29s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.62it/s]\n",
      "Processing Dataset:  16%|█▌        | 187/1195 [04:23<22:34,  1.34s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.14it/s]\n",
      "Processing Dataset:  16%|█▌        | 188/1195 [04:24<21:16,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.84it/s]\n",
      "Processing Dataset:  16%|█▌        | 189/1195 [04:25<21:29,  1.28s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.97it/s]\n",
      "Processing Dataset:  16%|█▌        | 190/1195 [04:26<21:28,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.31it/s]\n",
      "Processing Dataset:  16%|█▌        | 191/1195 [04:28<21:03,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.59it/s]\n",
      "Processing Dataset:  16%|█▌        | 192/1195 [04:29<21:16,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.80it/s]\n",
      "Processing Dataset:  16%|█▌        | 193/1195 [04:30<20:42,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.02it/s]\n",
      "Processing Dataset:  16%|█▌        | 194/1195 [04:31<20:04,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 27.30it/s]\n",
      "Processing Dataset:  16%|█▋        | 195/1195 [04:32<19:15,  1.16s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 44.43it/s]\n",
      "Processing Dataset:  16%|█▋        | 196/1195 [04:33<18:39,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.04it/s]\n",
      "Processing Dataset:  16%|█▋        | 197/1195 [04:35<19:57,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 89.17it/s]\n",
      "Processing Dataset:  17%|█▋        | 198/1195 [04:36<20:31,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.53it/s]\n",
      "Processing Dataset:  17%|█▋        | 199/1195 [04:37<19:36,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 88.85it/s]\n",
      "Processing Dataset:  17%|█▋        | 200/1195 [04:38<19:36,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.95it/s]\n",
      "Processing Dataset:  17%|█▋        | 201/1195 [04:39<19:41,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.55it/s]\n",
      "Processing Dataset:  17%|█▋        | 202/1195 [04:41<19:42,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 35.02it/s]\n",
      "Processing Dataset:  17%|█▋        | 203/1195 [04:42<19:06,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.73it/s]\n",
      "Processing Dataset:  17%|█▋        | 204/1195 [04:43<19:05,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.22it/s]\n",
      "Processing Dataset:  17%|█▋        | 205/1195 [04:44<18:36,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.31it/s]\n",
      "Processing Dataset:  17%|█▋        | 206/1195 [04:45<19:12,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.30it/s]\n",
      "Processing Dataset:  17%|█▋        | 207/1195 [04:46<19:37,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.24it/s]\n",
      "Processing Dataset:  17%|█▋        | 208/1195 [04:47<19:04,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 87.79it/s]\n",
      "Processing Dataset:  17%|█▋        | 209/1195 [04:49<19:03,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.69it/s]\n",
      "Processing Dataset:  18%|█▊        | 210/1195 [04:50<19:09,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.77it/s]\n",
      "Processing Dataset:  18%|█▊        | 211/1195 [04:51<19:14,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.35it/s]\n",
      "Processing Dataset:  18%|█▊        | 212/1195 [04:52<19:12,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.93it/s]\n",
      "Processing Dataset:  18%|█▊        | 213/1195 [04:53<19:42,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.60it/s]\n",
      "Processing Dataset:  18%|█▊        | 214/1195 [04:55<19:25,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.65it/s]\n",
      "Processing Dataset:  18%|█▊        | 215/1195 [04:56<18:45,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 32.34it/s]\n",
      "Processing Dataset:  18%|█▊        | 216/1195 [04:57<18:59,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.95it/s]\n",
      "Processing Dataset:  18%|█▊        | 217/1195 [04:58<20:48,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.71it/s]\n",
      "Processing Dataset:  18%|█▊        | 218/1195 [04:59<19:58,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.42it/s]\n",
      "Processing Dataset:  18%|█▊        | 219/1195 [05:01<19:20,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.85it/s]\n",
      "Processing Dataset:  18%|█▊        | 220/1195 [05:02<18:43,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 86.96it/s]\n",
      "Processing Dataset:  18%|█▊        | 221/1195 [05:03<18:44,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 89.85it/s]\n",
      "Processing Dataset:  19%|█▊        | 222/1195 [05:04<18:50,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.21it/s]\n",
      "Processing Dataset:  19%|█▊        | 223/1195 [05:05<18:50,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.81it/s]\n",
      "Processing Dataset:  19%|█▊        | 224/1195 [05:06<18:46,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.04it/s]\n",
      "Processing Dataset:  19%|█▉        | 225/1195 [05:08<19:22,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.01it/s]\n",
      "Processing Dataset:  19%|█▉        | 226/1195 [05:09<18:43,  1.16s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 40.72it/s]\n",
      "Processing Dataset:  19%|█▉        | 227/1195 [05:10<18:23,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.50it/s]\n",
      "Processing Dataset:  19%|█▉        | 228/1195 [05:11<18:46,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.29it/s]\n",
      "Processing Dataset:  19%|█▉        | 229/1195 [05:12<18:56,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.27it/s]\n",
      "Processing Dataset:  19%|█▉        | 230/1195 [05:13<18:42,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.90it/s]\n",
      "Processing Dataset:  19%|█▉        | 231/1195 [05:15<18:52,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.91it/s]\n",
      "Processing Dataset:  19%|█▉        | 232/1195 [05:16<18:02,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.82it/s]\n",
      "Processing Dataset:  19%|█▉        | 233/1195 [05:17<18:09,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.80it/s]\n",
      "Processing Dataset:  20%|█▉        | 234/1195 [05:18<17:52,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.64it/s]\n",
      "Processing Dataset:  20%|█▉        | 235/1195 [05:19<18:50,  1.18s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.99it/s]\n",
      "Processing Dataset:  20%|█▉        | 236/1195 [05:20<19:53,  1.24s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 60.24it/s]\n",
      "Processing Dataset:  20%|█▉        | 237/1195 [05:22<19:33,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.00it/s]\n",
      "Processing Dataset:  20%|█▉        | 238/1195 [05:23<21:00,  1.32s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.94it/s]\n",
      "Processing Dataset:  20%|██        | 239/1195 [05:24<20:48,  1.31s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.97it/s]\n",
      "Processing Dataset:  20%|██        | 240/1195 [05:26<21:53,  1.38s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.54it/s]\n",
      "Processing Dataset:  20%|██        | 241/1195 [05:27<21:02,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.60it/s]\n",
      "Processing Dataset:  20%|██        | 242/1195 [05:28<19:38,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.26it/s]\n",
      "Processing Dataset:  20%|██        | 243/1195 [05:29<19:00,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.46it/s]\n",
      "Processing Dataset:  20%|██        | 244/1195 [05:31<18:42,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.09it/s]\n",
      "Processing Dataset:  21%|██        | 245/1195 [05:32<18:11,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 91.85it/s]\n",
      "Processing Dataset:  21%|██        | 246/1195 [05:33<18:56,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 33.81it/s]\n",
      "Processing Dataset:  21%|██        | 247/1195 [05:34<19:01,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.08it/s]\n",
      "Processing Dataset:  21%|██        | 248/1195 [05:35<19:17,  1.22s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 92.93it/s]\n",
      "Processing Dataset:  21%|██        | 249/1195 [05:37<20:02,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.48it/s]\n",
      "Processing Dataset:  21%|██        | 250/1195 [05:38<19:20,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 90.88it/s]\n",
      "Processing Dataset:  21%|██        | 251/1195 [05:39<18:42,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.95it/s]\n",
      "Processing Dataset:  21%|██        | 252/1195 [05:40<19:04,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.73it/s]\n",
      "Processing Dataset:  21%|██        | 253/1195 [05:42<19:14,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 89.50it/s]\n",
      "Processing Dataset:  21%|██▏       | 254/1195 [05:43<18:38,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.66it/s]\n",
      "Processing Dataset:  21%|██▏       | 255/1195 [05:44<18:17,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.70it/s]\n",
      "Processing Dataset:  21%|██▏       | 256/1195 [05:45<18:03,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.26it/s]\n",
      "Processing Dataset:  22%|██▏       | 257/1195 [05:46<19:15,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.50it/s]\n",
      "Processing Dataset:  22%|██▏       | 258/1195 [05:48<20:35,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.79it/s]\n",
      "Processing Dataset:  22%|██▏       | 259/1195 [05:49<19:32,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.06it/s]\n",
      "Processing Dataset:  22%|██▏       | 260/1195 [05:50<18:56,  1.22s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 62.01it/s]\n",
      "Processing Dataset:  22%|██▏       | 261/1195 [05:51<18:21,  1.18s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 88.70it/s]\n",
      "Processing Dataset:  22%|██▏       | 262/1195 [05:52<17:59,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.64it/s]\n",
      "Processing Dataset:  22%|██▏       | 263/1195 [05:53<18:30,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.90it/s]\n",
      "Processing Dataset:  22%|██▏       | 264/1195 [05:55<19:47,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.34it/s]\n",
      "Processing Dataset:  22%|██▏       | 265/1195 [05:56<19:11,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.49it/s]\n",
      "Processing Dataset:  22%|██▏       | 266/1195 [05:57<19:10,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.22it/s]\n",
      "Processing Dataset:  22%|██▏       | 267/1195 [05:59<19:20,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.44it/s]\n",
      "Processing Dataset:  22%|██▏       | 268/1195 [06:00<19:55,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.49it/s]\n",
      "Processing Dataset:  23%|██▎       | 269/1195 [06:01<19:55,  1.29s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.97it/s]\n",
      "Processing Dataset:  23%|██▎       | 270/1195 [06:03<19:57,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.05it/s]\n",
      "Processing Dataset:  23%|██▎       | 271/1195 [06:04<20:09,  1.31s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.42it/s]\n",
      "Processing Dataset:  23%|██▎       | 272/1195 [06:05<19:23,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.84it/s]\n",
      "Processing Dataset:  23%|██▎       | 273/1195 [06:06<18:59,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.26it/s]\n",
      "Processing Dataset:  23%|██▎       | 274/1195 [06:07<18:08,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 102.04it/s]\n",
      "Processing Dataset:  23%|██▎       | 275/1195 [06:09<22:01,  1.44s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 37.10it/s]\n",
      "Processing Dataset:  23%|██▎       | 276/1195 [06:11<21:34,  1.41s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.29it/s]\n",
      "Processing Dataset:  23%|██▎       | 277/1195 [06:12<21:38,  1.41s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.76it/s]\n",
      "Processing Dataset:  23%|██▎       | 278/1195 [06:13<21:18,  1.39s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 88.27it/s]\n",
      "Processing Dataset:  23%|██▎       | 279/1195 [06:15<20:57,  1.37s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.46it/s]\n",
      "Processing Dataset:  23%|██▎       | 280/1195 [06:16<19:51,  1.30s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 68.95it/s]\n",
      "Processing Dataset:  24%|██▎       | 281/1195 [06:17<20:02,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.57it/s]\n",
      "Processing Dataset:  24%|██▎       | 282/1195 [06:19<20:06,  1.32s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 86.75it/s]\n",
      "Processing Dataset:  24%|██▎       | 283/1195 [06:20<20:10,  1.33s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.27it/s]\n",
      "Processing Dataset:  24%|██▍       | 284/1195 [06:21<19:50,  1.31s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 77.74it/s]\n",
      "Processing Dataset:  24%|██▍       | 285/1195 [06:23<20:03,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.43it/s]\n",
      "Processing Dataset:  24%|██▍       | 286/1195 [06:24<20:09,  1.33s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.08it/s]\n",
      "Processing Dataset:  24%|██▍       | 287/1195 [06:25<19:03,  1.26s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 86.73it/s]\n",
      "Processing Dataset:  24%|██▍       | 288/1195 [06:26<19:27,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.37it/s]\n",
      "Processing Dataset:  24%|██▍       | 289/1195 [06:27<18:23,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.90it/s]\n",
      "Processing Dataset:  24%|██▍       | 290/1195 [06:29<18:49,  1.25s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 91.59it/s]\n",
      "Processing Dataset:  24%|██▍       | 291/1195 [06:30<18:54,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.58it/s]\n",
      "Processing Dataset:  24%|██▍       | 292/1195 [06:31<18:36,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.84it/s]\n",
      "Processing Dataset:  25%|██▍       | 293/1195 [06:32<18:30,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 30.21it/s]\n",
      "Processing Dataset:  25%|██▍       | 294/1195 [06:33<17:40,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.60it/s]\n",
      "Processing Dataset:  25%|██▍       | 295/1195 [06:35<18:28,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.75it/s]\n",
      "Processing Dataset:  25%|██▍       | 296/1195 [06:36<18:34,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.52it/s]\n",
      "Processing Dataset:  25%|██▍       | 297/1195 [06:37<18:04,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 99.58it/s]\n",
      "Processing Dataset:  25%|██▍       | 298/1195 [06:38<18:05,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.40it/s]\n",
      "Processing Dataset:  25%|██▌       | 299/1195 [06:40<18:10,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.41it/s]\n",
      "Processing Dataset:  25%|██▌       | 300/1195 [06:41<17:33,  1.18s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 64.09it/s]\n",
      "Processing Dataset:  25%|██▌       | 301/1195 [06:42<17:12,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.05it/s]\n",
      "Processing Dataset:  25%|██▌       | 302/1195 [06:43<16:57,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.53it/s]\n",
      "Processing Dataset:  25%|██▌       | 303/1195 [06:44<17:16,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.29it/s]\n",
      "Processing Dataset:  25%|██▌       | 304/1195 [06:45<17:36,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 32.21it/s]\n",
      "Processing Dataset:  26%|██▌       | 305/1195 [06:47<18:30,  1.25s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 47.22it/s]\n",
      "Processing Dataset:  26%|██▌       | 306/1195 [06:48<18:21,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 65.44it/s]\n",
      "Processing Dataset:  26%|██▌       | 307/1195 [06:49<17:46,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.73it/s]\n",
      "Processing Dataset:  26%|██▌       | 308/1195 [06:51<19:05,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.60it/s]\n",
      "Processing Dataset:  26%|██▌       | 309/1195 [06:52<19:34,  1.33s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.09it/s]\n",
      "Processing Dataset:  26%|██▌       | 310/1195 [06:53<19:20,  1.31s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.21it/s]\n",
      "Processing Dataset:  26%|██▌       | 311/1195 [06:55<19:01,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 34.18it/s]\n",
      "Processing Dataset:  26%|██▌       | 312/1195 [06:56<18:05,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.87it/s]\n",
      "Processing Dataset:  26%|██▌       | 313/1195 [06:57<17:09,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.69it/s]\n",
      "Processing Dataset:  26%|██▋       | 314/1195 [06:58<16:55,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 37.98it/s]\n",
      "Processing Dataset:  26%|██▋       | 315/1195 [06:59<17:24,  1.19s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 63.88it/s]\n",
      "Processing Dataset:  26%|██▋       | 316/1195 [07:00<18:18,  1.25s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 28.99it/s]\n",
      "Processing Dataset:  27%|██▋       | 317/1195 [07:01<16:49,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 62.67it/s]\n",
      "Processing Dataset:  27%|██▋       | 318/1195 [07:02<16:05,  1.10s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.52it/s]\n",
      "Processing Dataset:  27%|██▋       | 319/1195 [07:03<16:09,  1.11s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.77it/s]\n",
      "Processing Dataset:  27%|██▋       | 320/1195 [07:05<16:19,  1.12s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.99it/s]\n",
      "Processing Dataset:  27%|██▋       | 321/1195 [07:06<15:57,  1.10s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 28.14it/s]\n",
      "Processing Dataset:  27%|██▋       | 322/1195 [07:07<16:10,  1.11s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 96.88it/s]\n",
      "Processing Dataset:  27%|██▋       | 323/1195 [07:08<16:55,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.42it/s]\n",
      "Processing Dataset:  27%|██▋       | 324/1195 [07:09<17:05,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.10it/s]\n",
      "Processing Dataset:  27%|██▋       | 325/1195 [07:10<16:15,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.24it/s]\n",
      "Processing Dataset:  27%|██▋       | 326/1195 [07:12<17:19,  1.20s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 29.30it/s]\n",
      "Processing Dataset:  27%|██▋       | 327/1195 [07:13<16:47,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.36it/s]\n",
      "Processing Dataset:  27%|██▋       | 328/1195 [07:14<17:02,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.10it/s]\n",
      "Processing Dataset:  28%|██▊       | 329/1195 [07:15<17:34,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.01it/s]\n",
      "Processing Dataset:  28%|██▊       | 330/1195 [07:16<17:20,  1.20s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.60it/s]\n",
      "Processing Dataset:  28%|██▊       | 331/1195 [07:18<16:44,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.20it/s]\n",
      "Processing Dataset:  28%|██▊       | 332/1195 [07:19<16:33,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.64it/s]\n",
      "Processing Dataset:  28%|██▊       | 333/1195 [07:20<16:45,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.61it/s]\n",
      "Processing Dataset:  28%|██▊       | 334/1195 [07:21<16:36,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.53it/s]\n",
      "Processing Dataset:  28%|██▊       | 335/1195 [07:22<16:14,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 37.87it/s]\n",
      "Processing Dataset:  28%|██▊       | 336/1195 [07:23<16:55,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 56.72it/s]\n",
      "Processing Dataset:  28%|██▊       | 337/1195 [07:25<17:19,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 74.95it/s]\n",
      "Processing Dataset:  28%|██▊       | 338/1195 [07:26<17:50,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.16it/s]\n",
      "Processing Dataset:  28%|██▊       | 339/1195 [07:27<17:07,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 83.09it/s]\n",
      "Processing Dataset:  28%|██▊       | 340/1195 [07:28<17:02,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.84it/s]\n",
      "Processing Dataset:  29%|██▊       | 341/1195 [07:29<17:01,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.39it/s]\n",
      "Processing Dataset:  29%|██▊       | 342/1195 [07:31<16:47,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 65.70it/s]\n",
      "Processing Dataset:  29%|██▊       | 343/1195 [07:32<16:50,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.91it/s]\n",
      "Processing Dataset:  29%|██▉       | 344/1195 [07:33<16:41,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.34it/s]\n",
      "Processing Dataset:  29%|██▉       | 345/1195 [07:34<16:20,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 54.36it/s]\n",
      "Processing Dataset:  29%|██▉       | 346/1195 [07:35<17:11,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 26.78it/s]\n",
      "Processing Dataset:  29%|██▉       | 347/1195 [07:37<17:12,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.95it/s]\n",
      "Processing Dataset:  29%|██▉       | 348/1195 [07:38<17:09,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.94it/s]\n",
      "Processing Dataset:  29%|██▉       | 349/1195 [07:39<16:42,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.28it/s]\n",
      "Processing Dataset:  29%|██▉       | 350/1195 [07:40<16:17,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.64it/s]\n",
      "Processing Dataset:  29%|██▉       | 351/1195 [07:41<15:47,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.16it/s]\n",
      "Processing Dataset:  29%|██▉       | 352/1195 [07:42<16:11,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 74.25it/s]\n",
      "Processing Dataset:  30%|██▉       | 353/1195 [07:43<15:56,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.51it/s]\n",
      "Processing Dataset:  30%|██▉       | 354/1195 [07:44<15:14,  1.09s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 65.38it/s]\n",
      "Processing Dataset:  30%|██▉       | 355/1195 [07:45<14:45,  1.05s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 66.11it/s]\n",
      "Processing Dataset:  30%|██▉       | 356/1195 [07:47<15:29,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 56.99it/s]\n",
      "Processing Dataset:  30%|██▉       | 357/1195 [07:48<15:54,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.67it/s]\n",
      "Processing Dataset:  30%|██▉       | 358/1195 [07:49<16:00,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.80it/s]\n",
      "Processing Dataset:  30%|███       | 359/1195 [07:50<17:29,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.93it/s]\n",
      "Processing Dataset:  30%|███       | 360/1195 [07:52<16:59,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.25it/s]\n",
      "Processing Dataset:  30%|███       | 361/1195 [07:53<16:39,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.42it/s]\n",
      "Processing Dataset:  30%|███       | 362/1195 [07:54<15:47,  1.14s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.43it/s]\n",
      "Processing Dataset:  30%|███       | 363/1195 [07:55<16:31,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.10it/s]\n",
      "Processing Dataset:  30%|███       | 364/1195 [07:56<16:32,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.97it/s]\n",
      "Processing Dataset:  31%|███       | 365/1195 [07:57<16:03,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.55it/s]\n",
      "Processing Dataset:  31%|███       | 366/1195 [07:59<16:18,  1.18s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 27.13it/s]\n",
      "Processing Dataset:  31%|███       | 367/1195 [08:00<15:12,  1.10s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.94it/s]\n",
      "Processing Dataset:  31%|███       | 368/1195 [08:01<16:38,  1.21s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 65.48it/s]\n",
      "Processing Dataset:  31%|███       | 369/1195 [08:02<16:31,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.70it/s]\n",
      "Processing Dataset:  31%|███       | 370/1195 [08:03<16:06,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.88it/s]\n",
      "Processing Dataset:  31%|███       | 371/1195 [08:04<16:18,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.28it/s]\n",
      "Processing Dataset:  31%|███       | 372/1195 [08:06<15:53,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 88.51it/s]\n",
      "Processing Dataset:  31%|███       | 373/1195 [08:07<16:54,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.17it/s]\n",
      "Processing Dataset:  31%|███▏      | 374/1195 [08:08<16:25,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.62it/s]\n",
      "Processing Dataset:  31%|███▏      | 375/1195 [08:09<15:59,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.42it/s]\n",
      "Processing Dataset:  31%|███▏      | 376/1195 [08:10<16:14,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.55it/s]\n",
      "Processing Dataset:  32%|███▏      | 377/1195 [08:11<15:38,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.39it/s]\n",
      "Processing Dataset:  32%|███▏      | 378/1195 [08:13<15:47,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.44it/s]\n",
      "Processing Dataset:  32%|███▏      | 379/1195 [08:14<17:09,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.02it/s]\n",
      "Processing Dataset:  32%|███▏      | 380/1195 [08:15<16:41,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.33it/s]\n",
      "Processing Dataset:  32%|███▏      | 381/1195 [08:17<16:40,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.62it/s]\n",
      "Processing Dataset:  32%|███▏      | 382/1195 [08:18<16:31,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.17it/s]\n",
      "Processing Dataset:  32%|███▏      | 383/1195 [08:19<16:15,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.95it/s]\n",
      "Processing Dataset:  32%|███▏      | 384/1195 [08:20<16:31,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.14it/s]\n",
      "Processing Dataset:  32%|███▏      | 385/1195 [08:21<16:03,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.13it/s]\n",
      "Processing Dataset:  32%|███▏      | 386/1195 [08:23<16:24,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.44it/s]\n",
      "Processing Dataset:  32%|███▏      | 387/1195 [08:24<16:24,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 25.52it/s]\n",
      "Processing Dataset:  32%|███▏      | 388/1195 [08:25<16:26,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.51it/s]\n",
      "Processing Dataset:  33%|███▎      | 389/1195 [08:26<17:21,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.94it/s]\n",
      "Processing Dataset:  33%|███▎      | 390/1195 [08:28<17:02,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.17it/s]\n",
      "Processing Dataset:  33%|███▎      | 391/1195 [08:29<16:13,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.63it/s]\n",
      "Processing Dataset:  33%|███▎      | 392/1195 [08:30<16:41,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.14it/s]\n",
      "Processing Dataset:  33%|███▎      | 393/1195 [08:31<16:44,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.95it/s]\n",
      "Processing Dataset:  33%|███▎      | 394/1195 [08:32<16:09,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.11it/s]\n",
      "Processing Dataset:  33%|███▎      | 395/1195 [08:34<16:39,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 52.20it/s]\n",
      "Processing Dataset:  33%|███▎      | 396/1195 [08:35<15:47,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.59it/s]\n",
      "Processing Dataset:  33%|███▎      | 397/1195 [08:36<15:33,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.09it/s]\n",
      "Processing Dataset:  33%|███▎      | 398/1195 [08:37<15:58,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 86.29it/s]\n",
      "Processing Dataset:  33%|███▎      | 399/1195 [08:39<16:48,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.31it/s]\n",
      "Processing Dataset:  33%|███▎      | 400/1195 [08:40<16:17,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.00it/s]\n",
      "Processing Dataset:  34%|███▎      | 401/1195 [08:41<16:02,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.16it/s]\n",
      "Processing Dataset:  34%|███▎      | 402/1195 [08:42<15:43,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.79it/s]\n",
      "Processing Dataset:  34%|███▎      | 403/1195 [08:43<15:48,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.45it/s]\n",
      "Processing Dataset:  34%|███▍      | 404/1195 [08:44<14:59,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.20it/s]\n",
      "Processing Dataset:  34%|███▍      | 405/1195 [08:45<14:43,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.66it/s]\n",
      "Processing Dataset:  34%|███▍      | 406/1195 [08:47<15:20,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.29it/s]\n",
      "Processing Dataset:  34%|███▍      | 407/1195 [08:48<16:22,  1.25s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 60.48it/s]\n",
      "Processing Dataset:  34%|███▍      | 408/1195 [08:50<17:04,  1.30s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 61.81it/s]\n",
      "Processing Dataset:  34%|███▍      | 409/1195 [08:51<16:57,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.96it/s]\n",
      "Processing Dataset:  34%|███▍      | 410/1195 [08:52<16:07,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 65.71it/s]\n",
      "Processing Dataset:  34%|███▍      | 411/1195 [08:53<15:59,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.91it/s]\n",
      "Processing Dataset:  34%|███▍      | 412/1195 [08:57<26:19,  2.02s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.12it/s]\n",
      "Processing Dataset:  35%|███▍      | 413/1195 [08:58<22:51,  1.75s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.73it/s]\n",
      "Processing Dataset:  35%|███▍      | 414/1195 [08:59<20:41,  1.59s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.05it/s]\n",
      "Processing Dataset:  35%|███▍      | 415/1195 [09:00<18:51,  1.45s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 30.69it/s]\n",
      "Processing Dataset:  35%|███▍      | 416/1195 [09:02<18:15,  1.41s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.93it/s]\n",
      "Processing Dataset:  35%|███▍      | 417/1195 [09:03<18:31,  1.43s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.74it/s]\n",
      "Processing Dataset:  35%|███▍      | 418/1195 [09:04<17:23,  1.34s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 82.39it/s]\n",
      "Processing Dataset:  35%|███▌      | 419/1195 [09:05<16:06,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.18it/s]\n",
      "Processing Dataset:  35%|███▌      | 420/1195 [09:07<15:26,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 65.49it/s]\n",
      "Processing Dataset:  35%|███▌      | 421/1195 [09:08<15:37,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 54.15it/s]\n",
      "Processing Dataset:  35%|███▌      | 422/1195 [09:09<15:40,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 73.89it/s]\n",
      "Processing Dataset:  35%|███▌      | 423/1195 [09:10<16:29,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 76.52it/s]\n",
      "Processing Dataset:  35%|███▌      | 424/1195 [09:12<16:22,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.82it/s]\n",
      "Processing Dataset:  36%|███▌      | 425/1195 [09:13<16:25,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.58it/s]\n",
      "Processing Dataset:  36%|███▌      | 426/1195 [09:14<16:25,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.00it/s]\n",
      "Processing Dataset:  36%|███▌      | 427/1195 [09:16<16:28,  1.29s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 71.37it/s]\n",
      "Processing Dataset:  36%|███▌      | 428/1195 [09:17<16:28,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.35it/s]\n",
      "Processing Dataset:  36%|███▌      | 429/1195 [09:18<16:00,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.09it/s]\n",
      "Processing Dataset:  36%|███▌      | 430/1195 [09:19<16:07,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.10it/s]\n",
      "Processing Dataset:  36%|███▌      | 431/1195 [09:20<15:36,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 82.36it/s]\n",
      "Processing Dataset:  36%|███▌      | 432/1195 [09:22<15:47,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.38it/s]\n",
      "Processing Dataset:  36%|███▌      | 433/1195 [09:23<15:27,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.80it/s]\n",
      "Processing Dataset:  36%|███▋      | 434/1195 [09:24<15:20,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.32it/s]\n",
      "Processing Dataset:  36%|███▋      | 435/1195 [09:25<14:43,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 71.17it/s]\n",
      "Processing Dataset:  36%|███▋      | 436/1195 [09:27<16:15,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.57it/s]\n",
      "Processing Dataset:  37%|███▋      | 437/1195 [09:28<16:08,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.32it/s]\n",
      "Processing Dataset:  37%|███▋      | 438/1195 [09:29<15:43,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.07it/s]\n",
      "Processing Dataset:  37%|███▋      | 439/1195 [09:30<15:17,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.54it/s]\n",
      "Processing Dataset:  37%|███▋      | 440/1195 [09:31<14:40,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 76.81it/s]\n",
      "Processing Dataset:  37%|███▋      | 441/1195 [09:33<14:43,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.29it/s]\n",
      "Processing Dataset:  37%|███▋      | 442/1195 [09:34<14:25,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.03it/s]\n",
      "Processing Dataset:  37%|███▋      | 443/1195 [09:35<14:47,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.45it/s]\n",
      "Processing Dataset:  37%|███▋      | 444/1195 [09:36<14:57,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.42it/s]\n",
      "Processing Dataset:  37%|███▋      | 445/1195 [09:37<14:50,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.42it/s]\n",
      "Processing Dataset:  37%|███▋      | 446/1195 [09:38<14:15,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.84it/s]\n",
      "Processing Dataset:  37%|███▋      | 447/1195 [09:40<14:35,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.70it/s]\n",
      "Processing Dataset:  37%|███▋      | 448/1195 [09:41<14:16,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.37it/s]\n",
      "Processing Dataset:  38%|███▊      | 449/1195 [09:42<14:07,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.96it/s]\n",
      "Processing Dataset:  38%|███▊      | 450/1195 [09:43<14:10,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 73.29it/s]\n",
      "Processing Dataset:  38%|███▊      | 451/1195 [09:44<14:45,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.97it/s]\n",
      "Processing Dataset:  38%|███▊      | 452/1195 [09:45<14:21,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.61it/s]\n",
      "Processing Dataset:  38%|███▊      | 453/1195 [09:47<14:31,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 66.73it/s]\n",
      "Processing Dataset:  38%|███▊      | 454/1195 [09:48<15:10,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.42it/s]\n",
      "Processing Dataset:  38%|███▊      | 455/1195 [09:49<14:48,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 73.10it/s]\n",
      "Processing Dataset:  38%|███▊      | 456/1195 [09:50<15:18,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.03it/s]\n",
      "Processing Dataset:  38%|███▊      | 457/1195 [09:52<15:12,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.99it/s]\n",
      "Processing Dataset:  38%|███▊      | 458/1195 [09:53<14:47,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.66it/s]\n",
      "Processing Dataset:  38%|███▊      | 459/1195 [09:54<14:31,  1.18s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 83.78it/s]\n",
      "Processing Dataset:  38%|███▊      | 460/1195 [09:55<14:23,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.07it/s]\n",
      "Processing Dataset:  39%|███▊      | 461/1195 [09:56<14:17,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.32it/s]\n",
      "Processing Dataset:  39%|███▊      | 462/1195 [09:57<14:07,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 82.42it/s]\n",
      "Processing Dataset:  39%|███▊      | 463/1195 [09:59<14:58,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.19it/s]\n",
      "Processing Dataset:  39%|███▉      | 464/1195 [10:00<14:37,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 82.87it/s]\n",
      "Processing Dataset:  39%|███▉      | 465/1195 [10:01<14:56,  1.23s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 48.72it/s]\n",
      "Processing Dataset:  39%|███▉      | 466/1195 [10:02<14:14,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.44it/s]\n",
      "Processing Dataset:  39%|███▉      | 467/1195 [10:03<14:35,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.03it/s]\n",
      "Processing Dataset:  39%|███▉      | 468/1195 [10:05<15:48,  1.30s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 88.06it/s]\n",
      "Processing Dataset:  39%|███▉      | 469/1195 [10:06<15:50,  1.31s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.74it/s]\n",
      "Processing Dataset:  39%|███▉      | 470/1195 [10:08<15:52,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.88it/s]\n",
      "Processing Dataset:  39%|███▉      | 471/1195 [10:09<15:43,  1.30s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.56it/s]\n",
      "Processing Dataset:  39%|███▉      | 472/1195 [10:10<16:00,  1.33s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.61it/s]\n",
      "Processing Dataset:  40%|███▉      | 473/1195 [10:11<15:37,  1.30s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.58it/s]\n",
      "Processing Dataset:  40%|███▉      | 474/1195 [10:12<14:23,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.57it/s]\n",
      "Processing Dataset:  40%|███▉      | 475/1195 [10:14<14:15,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 30.02it/s]\n",
      "Processing Dataset:  40%|███▉      | 476/1195 [10:15<13:52,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 64.02it/s]\n",
      "Processing Dataset:  40%|███▉      | 477/1195 [10:16<14:44,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.41it/s]\n",
      "Processing Dataset:  40%|████      | 478/1195 [10:17<14:45,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 57.71it/s]\n",
      "Processing Dataset:  40%|████      | 479/1195 [10:18<13:59,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.87it/s]\n",
      "Processing Dataset:  40%|████      | 480/1195 [10:19<13:44,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 49.53it/s]\n",
      "Processing Dataset:  40%|████      | 481/1195 [10:21<13:22,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.11it/s]\n",
      "Processing Dataset:  40%|████      | 482/1195 [10:22<13:57,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.75it/s]\n",
      "Processing Dataset:  40%|████      | 483/1195 [10:23<14:24,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 81.48it/s]\n",
      "Processing Dataset:  41%|████      | 484/1195 [10:24<14:12,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.41it/s]\n",
      "Processing Dataset:  41%|████      | 485/1195 [10:25<13:37,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 61.70it/s]\n",
      "Processing Dataset:  41%|████      | 486/1195 [10:26<13:14,  1.12s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.47it/s]\n",
      "Processing Dataset:  41%|████      | 487/1195 [10:28<13:34,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 63.18it/s]\n",
      "Processing Dataset:  41%|████      | 488/1195 [10:29<14:03,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.12it/s]\n",
      "Processing Dataset:  41%|████      | 489/1195 [10:30<13:57,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.26it/s]\n",
      "Processing Dataset:  41%|████      | 490/1195 [10:31<13:26,  1.14s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 27.45it/s]\n",
      "Processing Dataset:  41%|████      | 491/1195 [10:32<12:45,  1.09s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 89.39it/s]\n",
      "Processing Dataset:  41%|████      | 492/1195 [10:33<13:34,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.70it/s]\n",
      "Processing Dataset:  41%|████▏     | 493/1195 [10:35<13:36,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.76it/s]\n",
      "Processing Dataset:  41%|████▏     | 494/1195 [10:36<13:37,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.77it/s]\n",
      "Processing Dataset:  41%|████▏     | 495/1195 [10:37<14:47,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 91.14it/s]\n",
      "Processing Dataset:  42%|████▏     | 496/1195 [10:39<14:47,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.39it/s]\n",
      "Processing Dataset:  42%|████▏     | 497/1195 [10:40<15:19,  1.32s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.85it/s]\n",
      "Processing Dataset:  42%|████▏     | 498/1195 [10:41<15:33,  1.34s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.36it/s]\n",
      "Processing Dataset:  42%|████▏     | 499/1195 [10:43<15:25,  1.33s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.62it/s]\n",
      "Processing Dataset:  42%|████▏     | 500/1195 [10:44<14:59,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.43it/s]\n",
      "Processing Dataset:  42%|████▏     | 501/1195 [10:45<14:28,  1.25s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.12it/s]\n",
      "Processing Dataset:  42%|████▏     | 502/1195 [10:46<14:20,  1.24s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 68.37it/s]\n",
      "Processing Dataset:  42%|████▏     | 503/1195 [10:47<14:23,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.74it/s]\n",
      "Processing Dataset:  42%|████▏     | 504/1195 [10:49<14:30,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.85it/s]\n",
      "Processing Dataset:  42%|████▏     | 505/1195 [10:50<14:23,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.59it/s]\n",
      "Processing Dataset:  42%|████▏     | 506/1195 [10:51<14:16,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 32.62it/s]\n",
      "Processing Dataset:  42%|████▏     | 507/1195 [10:53<14:48,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.19it/s]\n",
      "Processing Dataset:  43%|████▎     | 508/1195 [10:54<14:34,  1.27s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 79.90it/s]\n",
      "Processing Dataset:  43%|████▎     | 509/1195 [10:55<15:07,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.59it/s]\n",
      "Processing Dataset:  43%|████▎     | 510/1195 [10:57<14:54,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.15it/s]\n",
      "Processing Dataset:  43%|████▎     | 511/1195 [10:58<14:56,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.18it/s]\n",
      "Processing Dataset:  43%|████▎     | 512/1195 [10:59<14:37,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.41it/s]\n",
      "Processing Dataset:  43%|████▎     | 513/1195 [11:00<14:42,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.36it/s]\n",
      "Processing Dataset:  43%|████▎     | 514/1195 [11:01<13:31,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.33it/s]\n",
      "Processing Dataset:  43%|████▎     | 515/1195 [11:03<14:06,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.32it/s]\n",
      "Processing Dataset:  43%|████▎     | 516/1195 [11:04<13:36,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.98it/s]\n",
      "Processing Dataset:  43%|████▎     | 517/1195 [11:05<13:48,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.55it/s]\n",
      "Processing Dataset:  43%|████▎     | 518/1195 [11:06<13:51,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.57it/s]\n",
      "Processing Dataset:  43%|████▎     | 519/1195 [11:08<13:39,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.89it/s]\n",
      "Processing Dataset:  44%|████▎     | 520/1195 [11:09<13:10,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.90it/s]\n",
      "Processing Dataset:  44%|████▎     | 521/1195 [11:10<13:26,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.29it/s]\n",
      "Processing Dataset:  44%|████▎     | 522/1195 [11:11<13:00,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.45it/s]\n",
      "Processing Dataset:  44%|████▍     | 523/1195 [11:12<13:27,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.71it/s]\n",
      "Processing Dataset:  44%|████▍     | 524/1195 [11:14<13:50,  1.24s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 79.96it/s]\n",
      "Processing Dataset:  44%|████▍     | 525/1195 [11:15<13:45,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 63.42it/s]\n",
      "Processing Dataset:  44%|████▍     | 526/1195 [11:16<13:38,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 54.09it/s]\n",
      "Processing Dataset:  44%|████▍     | 527/1195 [11:17<13:26,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 42.39it/s]\n",
      "Processing Dataset:  44%|████▍     | 528/1195 [11:18<13:49,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.66it/s]\n",
      "Processing Dataset:  44%|████▍     | 529/1195 [11:20<13:21,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.75it/s]\n",
      "Processing Dataset:  44%|████▍     | 530/1195 [11:21<13:15,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.54it/s]\n",
      "Processing Dataset:  44%|████▍     | 531/1195 [11:22<12:45,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.93it/s]\n",
      "Processing Dataset:  45%|████▍     | 532/1195 [11:23<13:21,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.44it/s]\n",
      "Processing Dataset:  45%|████▍     | 533/1195 [11:24<12:48,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.09it/s]\n",
      "Processing Dataset:  45%|████▍     | 534/1195 [11:25<12:44,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.91it/s]\n",
      "Processing Dataset:  45%|████▍     | 535/1195 [11:27<13:13,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 65.78it/s]\n",
      "Processing Dataset:  45%|████▍     | 536/1195 [11:28<13:41,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.39it/s]\n",
      "Processing Dataset:  45%|████▍     | 537/1195 [11:29<13:45,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 66.51it/s]\n",
      "Processing Dataset:  45%|████▌     | 538/1195 [11:31<14:21,  1.31s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.80it/s]\n",
      "Processing Dataset:  45%|████▌     | 539/1195 [11:32<13:30,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.65it/s]\n",
      "Processing Dataset:  45%|████▌     | 540/1195 [11:33<13:05,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.47it/s]\n",
      "Processing Dataset:  45%|████▌     | 541/1195 [11:34<13:09,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.05it/s]\n",
      "Processing Dataset:  45%|████▌     | 542/1195 [11:35<13:12,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.67it/s]\n",
      "Processing Dataset:  45%|████▌     | 543/1195 [11:37<13:03,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.30it/s]\n",
      "Processing Dataset:  46%|████▌     | 544/1195 [11:38<12:44,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 84.84it/s]\n",
      "Processing Dataset:  46%|████▌     | 545/1195 [11:39<12:46,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.31it/s]\n",
      "Processing Dataset:  46%|████▌     | 546/1195 [11:40<13:17,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.46it/s]\n",
      "Processing Dataset:  46%|████▌     | 547/1195 [11:42<13:43,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.00it/s]\n",
      "Processing Dataset:  46%|████▌     | 548/1195 [11:43<14:01,  1.30s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.64it/s]\n",
      "Processing Dataset:  46%|████▌     | 549/1195 [11:44<14:12,  1.32s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 82.44it/s]\n",
      "Processing Dataset:  46%|████▌     | 550/1195 [11:46<13:58,  1.30s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.19it/s]\n",
      "Processing Dataset:  46%|████▌     | 551/1195 [11:47<14:05,  1.31s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 81.88it/s]\n",
      "Processing Dataset:  46%|████▌     | 552/1195 [11:48<13:56,  1.30s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.04it/s]\n",
      "Processing Dataset:  46%|████▋     | 553/1195 [11:49<13:02,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.86it/s]\n",
      "Processing Dataset:  46%|████▋     | 554/1195 [11:50<12:55,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.49it/s]\n",
      "Processing Dataset:  46%|████▋     | 555/1195 [11:51<12:31,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 29.26it/s]\n",
      "Processing Dataset:  47%|████▋     | 556/1195 [11:53<12:42,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 24.95it/s]\n",
      "Processing Dataset:  47%|████▋     | 557/1195 [11:54<12:43,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 79.61it/s]\n",
      "Processing Dataset:  47%|████▋     | 558/1195 [11:55<13:39,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.41it/s]\n",
      "Processing Dataset:  47%|████▋     | 559/1195 [11:57<13:12,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.12it/s]\n",
      "Processing Dataset:  47%|████▋     | 560/1195 [11:58<13:03,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.92it/s]\n",
      "Processing Dataset:  47%|████▋     | 561/1195 [11:59<12:53,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.89it/s]\n",
      "Processing Dataset:  47%|████▋     | 562/1195 [12:00<12:18,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.59it/s]\n",
      "Processing Dataset:  47%|████▋     | 563/1195 [12:01<12:04,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 69.37it/s]\n",
      "Processing Dataset:  47%|████▋     | 564/1195 [12:02<11:45,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.18it/s]\n",
      "Processing Dataset:  47%|████▋     | 565/1195 [12:03<11:33,  1.10s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.05it/s]\n",
      "Processing Dataset:  47%|████▋     | 566/1195 [12:05<12:13,  1.17s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 77.49it/s]\n",
      "Processing Dataset:  47%|████▋     | 567/1195 [12:06<13:06,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.35it/s]\n",
      "Processing Dataset:  48%|████▊     | 568/1195 [12:07<13:29,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.84it/s]\n",
      "Processing Dataset:  48%|████▊     | 569/1195 [12:09<13:20,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.65it/s]\n",
      "Processing Dataset:  48%|████▊     | 570/1195 [12:10<12:33,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.13it/s]\n",
      "Processing Dataset:  48%|████▊     | 571/1195 [12:11<12:51,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.82it/s]\n",
      "Processing Dataset:  48%|████▊     | 572/1195 [12:12<12:55,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.25it/s]\n",
      "Processing Dataset:  48%|████▊     | 573/1195 [12:13<12:36,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.41it/s]\n",
      "Processing Dataset:  48%|████▊     | 574/1195 [12:15<12:22,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.38it/s]\n",
      "Processing Dataset:  48%|████▊     | 575/1195 [12:16<12:41,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 37.04it/s]\n",
      "Processing Dataset:  48%|████▊     | 576/1195 [12:17<12:58,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.37it/s]\n",
      "Processing Dataset:  48%|████▊     | 577/1195 [12:18<12:26,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.23it/s]\n",
      "Processing Dataset:  48%|████▊     | 578/1195 [12:20<13:18,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.21it/s]\n",
      "Processing Dataset:  48%|████▊     | 579/1195 [12:21<12:44,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.14it/s]\n",
      "Processing Dataset:  49%|████▊     | 580/1195 [12:22<12:37,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 65.08it/s]\n",
      "Processing Dataset:  49%|████▊     | 581/1195 [12:23<12:07,  1.18s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 81.44it/s]\n",
      "Processing Dataset:  49%|████▊     | 582/1195 [12:24<12:15,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 74.16it/s]\n",
      "Processing Dataset:  49%|████▉     | 583/1195 [12:26<12:44,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.58it/s]\n",
      "Processing Dataset:  49%|████▉     | 584/1195 [12:27<11:57,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.00it/s]\n",
      "Processing Dataset:  49%|████▉     | 585/1195 [12:28<12:00,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.59it/s]\n",
      "Processing Dataset:  49%|████▉     | 586/1195 [12:29<12:18,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.98it/s]\n",
      "Processing Dataset:  49%|████▉     | 587/1195 [12:31<12:35,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.13it/s]\n",
      "Processing Dataset:  49%|████▉     | 588/1195 [12:32<12:22,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.34it/s]\n",
      "Processing Dataset:  49%|████▉     | 589/1195 [12:33<11:57,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.81it/s]\n",
      "Processing Dataset:  49%|████▉     | 590/1195 [12:34<11:21,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.11it/s]\n",
      "Processing Dataset:  49%|████▉     | 591/1195 [12:35<11:19,  1.13s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.54it/s]\n",
      "Processing Dataset:  50%|████▉     | 592/1195 [12:36<11:58,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.39it/s]\n",
      "Processing Dataset:  50%|████▉     | 593/1195 [12:38<12:18,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.28it/s]\n",
      "Processing Dataset:  50%|████▉     | 594/1195 [12:39<12:11,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 71.06it/s]\n",
      "Processing Dataset:  50%|████▉     | 595/1195 [12:40<12:01,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.56it/s]\n",
      "Processing Dataset:  50%|████▉     | 596/1195 [12:41<11:47,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.33it/s]\n",
      "Processing Dataset:  50%|████▉     | 597/1195 [12:42<12:05,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.31it/s]\n",
      "Processing Dataset:  50%|█████     | 598/1195 [12:44<12:24,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.52it/s]\n",
      "Processing Dataset:  50%|█████     | 599/1195 [12:45<12:14,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 77.22it/s]\n",
      "Processing Dataset:  50%|█████     | 600/1195 [12:46<12:24,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.66it/s]\n",
      "Processing Dataset:  50%|█████     | 601/1195 [12:47<12:24,  1.25s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 70.60it/s]\n",
      "Processing Dataset:  50%|█████     | 602/1195 [12:49<11:55,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.11it/s]\n",
      "Processing Dataset:  50%|█████     | 603/1195 [12:50<11:39,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.20it/s]\n",
      "Processing Dataset:  51%|█████     | 604/1195 [12:51<11:51,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.11it/s]\n",
      "Processing Dataset:  51%|█████     | 605/1195 [12:52<12:07,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.27it/s]\n",
      "Processing Dataset:  51%|█████     | 606/1195 [12:53<11:52,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.21it/s]\n",
      "Processing Dataset:  51%|█████     | 607/1195 [12:55<12:01,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.33it/s]\n",
      "Processing Dataset:  51%|█████     | 608/1195 [12:56<12:51,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 38.85it/s]\n",
      "Processing Dataset:  51%|█████     | 609/1195 [12:58<13:05,  1.34s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.82it/s]\n",
      "Processing Dataset:  51%|█████     | 610/1195 [12:59<12:21,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.52it/s]\n",
      "Processing Dataset:  51%|█████     | 611/1195 [13:00<11:51,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.94it/s]\n",
      "Processing Dataset:  51%|█████     | 612/1195 [13:01<11:40,  1.20s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 63.48it/s]\n",
      "Processing Dataset:  51%|█████▏    | 613/1195 [13:02<11:07,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.29it/s]\n",
      "Processing Dataset:  51%|█████▏    | 614/1195 [13:03<11:13,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 73.74it/s]\n",
      "Processing Dataset:  51%|█████▏    | 615/1195 [13:04<11:41,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.32it/s]\n",
      "Processing Dataset:  52%|█████▏    | 616/1195 [13:06<13:10,  1.37s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 58.57it/s]\n",
      "Processing Dataset:  52%|█████▏    | 617/1195 [13:08<13:52,  1.44s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 88.84it/s]\n",
      "Processing Dataset:  52%|█████▏    | 618/1195 [13:09<13:10,  1.37s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.00it/s]\n",
      "Processing Dataset:  52%|█████▏    | 619/1195 [13:10<12:47,  1.33s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.17it/s]\n",
      "Processing Dataset:  52%|█████▏    | 620/1195 [13:11<12:33,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.65it/s]\n",
      "Processing Dataset:  52%|█████▏    | 621/1195 [13:13<12:09,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.35it/s]\n",
      "Processing Dataset:  52%|█████▏    | 622/1195 [13:14<11:48,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 66.20it/s]\n",
      "Processing Dataset:  52%|█████▏    | 623/1195 [13:15<11:56,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.37it/s]\n",
      "Processing Dataset:  52%|█████▏    | 624/1195 [13:16<11:25,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.89it/s]\n",
      "Processing Dataset:  52%|█████▏    | 625/1195 [13:17<11:37,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.30it/s]\n",
      "Processing Dataset:  52%|█████▏    | 626/1195 [13:18<10:49,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.51it/s]\n",
      "Processing Dataset:  52%|█████▏    | 627/1195 [13:20<11:06,  1.17s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 86.21it/s]\n",
      "Processing Dataset:  53%|█████▎    | 628/1195 [13:21<11:29,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.50it/s]\n",
      "Processing Dataset:  53%|█████▎    | 629/1195 [13:22<11:03,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.01it/s]\n",
      "Processing Dataset:  53%|█████▎    | 630/1195 [13:23<11:04,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.12it/s]\n",
      "Processing Dataset:  53%|█████▎    | 631/1195 [13:25<11:18,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.38it/s]\n",
      "Processing Dataset:  53%|█████▎    | 632/1195 [13:26<11:20,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.65it/s]\n",
      "Processing Dataset:  53%|█████▎    | 633/1195 [13:27<11:22,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.40it/s]\n",
      "Processing Dataset:  53%|█████▎    | 634/1195 [13:28<11:19,  1.21s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 44.79it/s]\n",
      "Processing Dataset:  53%|█████▎    | 635/1195 [13:29<10:50,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 84.43it/s]\n",
      "Processing Dataset:  53%|█████▎    | 636/1195 [13:30<10:45,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.66it/s]\n",
      "Processing Dataset:  53%|█████▎    | 637/1195 [13:32<11:11,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.78it/s]\n",
      "Processing Dataset:  53%|█████▎    | 638/1195 [13:33<11:22,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.18it/s]\n",
      "Processing Dataset:  53%|█████▎    | 639/1195 [13:34<11:06,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.87it/s]\n",
      "Processing Dataset:  54%|█████▎    | 640/1195 [13:35<10:51,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.49it/s]\n",
      "Processing Dataset:  54%|█████▎    | 641/1195 [13:36<10:59,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 23.69it/s]\n",
      "Processing Dataset:  54%|█████▎    | 642/1195 [13:37<10:36,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 79.54it/s]\n",
      "Processing Dataset:  54%|█████▍    | 643/1195 [13:38<10:13,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.14it/s]\n",
      "Processing Dataset:  54%|█████▍    | 644/1195 [13:40<10:46,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.19it/s]\n",
      "Processing Dataset:  54%|█████▍    | 645/1195 [13:41<10:43,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.91it/s]\n",
      "Processing Dataset:  54%|█████▍    | 646/1195 [13:42<10:32,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.01it/s]\n",
      "Processing Dataset:  54%|█████▍    | 647/1195 [13:43<10:10,  1.11s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.71it/s]\n",
      "Processing Dataset:  54%|█████▍    | 648/1195 [13:44<10:19,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.86it/s]\n",
      "Processing Dataset:  54%|█████▍    | 649/1195 [13:46<10:53,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.14it/s]\n",
      "Processing Dataset:  54%|█████▍    | 650/1195 [13:47<10:29,  1.15s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 68.32it/s]\n",
      "Processing Dataset:  54%|█████▍    | 651/1195 [13:48<09:59,  1.10s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.18it/s]\n",
      "Processing Dataset:  55%|█████▍    | 652/1195 [13:49<10:10,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.46it/s]\n",
      "Processing Dataset:  55%|█████▍    | 653/1195 [13:50<10:29,  1.16s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.87it/s]\n",
      "Processing Dataset:  55%|█████▍    | 654/1195 [13:51<10:01,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.07it/s]\n",
      "Processing Dataset:  55%|█████▍    | 655/1195 [13:52<10:05,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.20it/s]\n",
      "Processing Dataset:  55%|█████▍    | 656/1195 [13:54<10:28,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.05it/s]\n",
      "Processing Dataset:  55%|█████▍    | 657/1195 [13:55<11:09,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.19it/s]\n",
      "Processing Dataset:  55%|█████▌    | 658/1195 [13:56<11:33,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.03it/s]\n",
      "Processing Dataset:  55%|█████▌    | 659/1195 [13:58<11:43,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 76.09it/s]\n",
      "Processing Dataset:  55%|█████▌    | 660/1195 [13:59<11:53,  1.33s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.65it/s]\n",
      "Processing Dataset:  55%|█████▌    | 661/1195 [14:00<11:20,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.91it/s]\n",
      "Processing Dataset:  55%|█████▌    | 662/1195 [14:01<11:04,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.35it/s]\n",
      "Processing Dataset:  55%|█████▌    | 663/1195 [14:03<11:02,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 71.82it/s]\n",
      "Processing Dataset:  56%|█████▌    | 664/1195 [14:04<10:53,  1.23s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 77.95it/s]\n",
      "Processing Dataset:  56%|█████▌    | 665/1195 [14:05<10:40,  1.21s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 21.41it/s]\n",
      "Processing Dataset:  56%|█████▌    | 666/1195 [14:06<10:15,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 64.43it/s]\n",
      "Processing Dataset:  56%|█████▌    | 667/1195 [14:07<10:27,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.38it/s]\n",
      "Processing Dataset:  56%|█████▌    | 668/1195 [14:09<10:48,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.12it/s]\n",
      "Processing Dataset:  56%|█████▌    | 669/1195 [14:10<11:21,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 44.37it/s]\n",
      "Processing Dataset:  56%|█████▌    | 670/1195 [14:11<10:48,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.47it/s]\n",
      "Processing Dataset:  56%|█████▌    | 671/1195 [14:12<10:29,  1.20s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 70.90it/s]\n",
      "Processing Dataset:  56%|█████▌    | 672/1195 [14:13<09:59,  1.15s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 66.87it/s]\n",
      "Processing Dataset:  56%|█████▋    | 673/1195 [14:15<10:04,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.01it/s]\n",
      "Processing Dataset:  56%|█████▋    | 674/1195 [14:16<09:50,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.44it/s]\n",
      "Processing Dataset:  56%|█████▋    | 675/1195 [14:17<09:22,  1.08s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.61it/s]\n",
      "Processing Dataset:  57%|█████▋    | 676/1195 [14:18<09:36,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.22it/s]\n",
      "Processing Dataset:  57%|█████▋    | 677/1195 [14:19<10:12,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 52.82it/s]\n",
      "Processing Dataset:  57%|█████▋    | 678/1195 [14:20<10:48,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 37.49it/s]\n",
      "Processing Dataset:  57%|█████▋    | 679/1195 [14:22<10:57,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.48it/s]\n",
      "Processing Dataset:  57%|█████▋    | 680/1195 [14:23<10:57,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.22it/s]\n",
      "Processing Dataset:  57%|█████▋    | 681/1195 [14:24<10:33,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.52it/s]\n",
      "Processing Dataset:  57%|█████▋    | 682/1195 [14:25<10:32,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.61it/s]\n",
      "Processing Dataset:  57%|█████▋    | 683/1195 [14:27<10:52,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 24.30it/s]\n",
      "Processing Dataset:  57%|█████▋    | 684/1195 [14:28<10:13,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.20it/s]\n",
      "Processing Dataset:  57%|█████▋    | 685/1195 [14:29<10:29,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.51it/s]\n",
      "Processing Dataset:  57%|█████▋    | 686/1195 [14:31<10:49,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.45it/s]\n",
      "Processing Dataset:  57%|█████▋    | 687/1195 [14:32<10:39,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 55.77it/s]\n",
      "Processing Dataset:  58%|█████▊    | 688/1195 [14:33<10:59,  1.30s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.25it/s]\n",
      "Processing Dataset:  58%|█████▊    | 689/1195 [14:34<10:51,  1.29s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.01it/s]\n",
      "Processing Dataset:  58%|█████▊    | 690/1195 [14:36<10:52,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.78it/s]\n",
      "Processing Dataset:  58%|█████▊    | 691/1195 [14:37<11:16,  1.34s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 91.54it/s]\n",
      "Processing Dataset:  58%|█████▊    | 692/1195 [14:38<10:43,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.81it/s]\n",
      "Processing Dataset:  58%|█████▊    | 693/1195 [14:39<10:02,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.21it/s]\n",
      "Processing Dataset:  58%|█████▊    | 694/1195 [14:41<10:28,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.69it/s]\n",
      "Processing Dataset:  58%|█████▊    | 695/1195 [14:42<10:14,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.66it/s]\n",
      "Processing Dataset:  58%|█████▊    | 696/1195 [14:43<09:57,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 32.42it/s]\n",
      "Processing Dataset:  58%|█████▊    | 697/1195 [14:44<09:59,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 65.47it/s]\n",
      "Processing Dataset:  58%|█████▊    | 698/1195 [14:46<10:20,  1.25s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 21.31it/s]\n",
      "Processing Dataset:  58%|█████▊    | 699/1195 [14:47<10:17,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.52it/s]\n",
      "Processing Dataset:  59%|█████▊    | 700/1195 [14:48<10:17,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 74.42it/s]\n",
      "Processing Dataset:  59%|█████▊    | 701/1195 [14:49<10:42,  1.30s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.27it/s]\n",
      "Processing Dataset:  59%|█████▊    | 702/1195 [14:51<11:02,  1.34s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 79.50it/s]\n",
      "Processing Dataset:  59%|█████▉    | 703/1195 [14:52<11:01,  1.34s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.88it/s]\n",
      "Processing Dataset:  59%|█████▉    | 704/1195 [14:53<10:19,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.16it/s]\n",
      "Processing Dataset:  59%|█████▉    | 705/1195 [14:55<10:12,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.62it/s]\n",
      "Processing Dataset:  59%|█████▉    | 706/1195 [14:56<10:01,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 64.47it/s]\n",
      "Processing Dataset:  59%|█████▉    | 707/1195 [14:57<10:21,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.85it/s]\n",
      "Processing Dataset:  59%|█████▉    | 708/1195 [14:58<10:23,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.50it/s]\n",
      "Processing Dataset:  59%|█████▉    | 709/1195 [14:59<09:37,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.98it/s]\n",
      "Processing Dataset:  59%|█████▉    | 710/1195 [15:01<09:42,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 79.51it/s]\n",
      "Processing Dataset:  59%|█████▉    | 711/1195 [15:02<10:15,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.70it/s]\n",
      "Processing Dataset:  60%|█████▉    | 712/1195 [15:03<09:56,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 30.96it/s]\n",
      "Processing Dataset:  60%|█████▉    | 713/1195 [15:04<09:52,  1.23s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 72.38it/s]\n",
      "Processing Dataset:  60%|█████▉    | 714/1195 [15:06<09:59,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.08it/s]\n",
      "Processing Dataset:  60%|█████▉    | 715/1195 [15:07<09:43,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 86.18it/s]\n",
      "Processing Dataset:  60%|█████▉    | 716/1195 [15:08<09:52,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.22it/s]\n",
      "Processing Dataset:  60%|██████    | 717/1195 [15:10<10:09,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 72.45it/s]\n",
      "Processing Dataset:  60%|██████    | 718/1195 [15:11<10:32,  1.33s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.33it/s]\n",
      "Processing Dataset:  60%|██████    | 719/1195 [15:12<10:06,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.07it/s]\n",
      "Processing Dataset:  60%|██████    | 720/1195 [15:13<10:13,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.20it/s]\n",
      "Processing Dataset:  60%|██████    | 721/1195 [15:15<09:45,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.51it/s]\n",
      "Processing Dataset:  60%|██████    | 722/1195 [15:16<09:14,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.74it/s]\n",
      "Processing Dataset:  61%|██████    | 723/1195 [15:17<08:47,  1.12s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 71.85it/s]\n",
      "Processing Dataset:  61%|██████    | 724/1195 [15:18<09:10,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 54.59it/s]\n",
      "Processing Dataset:  61%|██████    | 725/1195 [15:19<09:07,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 32.23it/s]\n",
      "Processing Dataset:  61%|██████    | 726/1195 [15:20<08:49,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.42it/s]\n",
      "Processing Dataset:  61%|██████    | 727/1195 [15:22<09:34,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 57.31it/s]\n",
      "Processing Dataset:  61%|██████    | 728/1195 [15:23<09:31,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.97it/s]\n",
      "Processing Dataset:  61%|██████    | 729/1195 [15:24<09:21,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.19it/s]\n",
      "Processing Dataset:  61%|██████    | 730/1195 [15:25<09:20,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.22it/s]\n",
      "Processing Dataset:  61%|██████    | 731/1195 [15:26<09:13,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.27it/s]\n",
      "Processing Dataset:  61%|██████▏   | 732/1195 [15:27<09:14,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.53it/s]\n",
      "Processing Dataset:  61%|██████▏   | 733/1195 [15:29<09:21,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.99it/s]\n",
      "Processing Dataset:  61%|██████▏   | 734/1195 [15:30<08:50,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.84it/s]\n",
      "Processing Dataset:  62%|██████▏   | 735/1195 [15:31<08:45,  1.14s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 73.28it/s]\n",
      "Processing Dataset:  62%|██████▏   | 736/1195 [15:32<09:11,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.59it/s]\n",
      "Processing Dataset:  62%|██████▏   | 737/1195 [15:33<09:14,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.86it/s]\n",
      "Processing Dataset:  62%|██████▏   | 738/1195 [15:35<09:23,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.05it/s]\n",
      "Processing Dataset:  62%|██████▏   | 739/1195 [15:36<09:06,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.41it/s]\n",
      "Processing Dataset:  62%|██████▏   | 740/1195 [15:37<08:52,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.27it/s]\n",
      "Processing Dataset:  62%|██████▏   | 741/1195 [15:38<08:40,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.58it/s]\n",
      "Processing Dataset:  62%|██████▏   | 742/1195 [15:39<08:32,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 41.15it/s]\n",
      "Processing Dataset:  62%|██████▏   | 743/1195 [15:40<08:56,  1.19s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.50it/s]\n",
      "Processing Dataset:  62%|██████▏   | 744/1195 [15:42<09:22,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.60it/s]\n",
      "Processing Dataset:  62%|██████▏   | 745/1195 [15:43<09:21,  1.25s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.05it/s]\n",
      "Processing Dataset:  62%|██████▏   | 746/1195 [15:44<08:51,  1.18s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 69.79it/s]\n",
      "Processing Dataset:  63%|██████▎   | 747/1195 [15:45<09:09,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 35.24it/s]\n",
      "Processing Dataset:  63%|██████▎   | 748/1195 [15:47<09:04,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.16it/s]\n",
      "Processing Dataset:  63%|██████▎   | 749/1195 [15:48<09:06,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.68it/s]\n",
      "Processing Dataset:  63%|██████▎   | 750/1195 [15:49<08:53,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 56.99it/s]\n",
      "Processing Dataset:  63%|██████▎   | 751/1195 [15:50<08:51,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.84it/s]\n",
      "Processing Dataset:  63%|██████▎   | 752/1195 [15:51<08:50,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.70it/s]\n",
      "Processing Dataset:  63%|██████▎   | 753/1195 [15:52<08:25,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.30it/s]\n",
      "Processing Dataset:  63%|██████▎   | 754/1195 [15:54<08:37,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.47it/s]\n",
      "Processing Dataset:  63%|██████▎   | 755/1195 [15:55<08:42,  1.19s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 72.34it/s]\n",
      "Processing Dataset:  63%|██████▎   | 756/1195 [15:56<08:49,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.36it/s]\n",
      "Processing Dataset:  63%|██████▎   | 757/1195 [15:57<08:45,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.16it/s]\n",
      "Processing Dataset:  63%|██████▎   | 758/1195 [15:58<08:41,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.86it/s]\n",
      "Processing Dataset:  64%|██████▎   | 759/1195 [16:00<08:49,  1.21s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.78it/s]\n",
      "Processing Dataset:  64%|██████▎   | 760/1195 [16:01<08:58,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.43it/s]\n",
      "Processing Dataset:  64%|██████▎   | 761/1195 [16:02<08:45,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.40it/s]\n",
      "Processing Dataset:  64%|██████▍   | 762/1195 [16:03<08:46,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.15it/s]\n",
      "Processing Dataset:  64%|██████▍   | 763/1195 [16:05<08:45,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.18it/s]\n",
      "Processing Dataset:  64%|██████▍   | 764/1195 [16:06<08:33,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.81it/s]\n",
      "Processing Dataset:  64%|██████▍   | 765/1195 [16:07<08:18,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.33it/s]\n",
      "Processing Dataset:  64%|██████▍   | 766/1195 [16:08<08:24,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.57it/s]\n",
      "Processing Dataset:  64%|██████▍   | 767/1195 [16:09<08:24,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.19it/s]\n",
      "Processing Dataset:  64%|██████▍   | 768/1195 [16:11<08:44,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 73.04it/s]\n",
      "Processing Dataset:  64%|██████▍   | 769/1195 [16:12<09:07,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.28it/s]\n",
      "Processing Dataset:  64%|██████▍   | 770/1195 [16:13<08:49,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.21it/s]\n",
      "Processing Dataset:  65%|██████▍   | 771/1195 [16:14<08:27,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.78it/s]\n",
      "Processing Dataset:  65%|██████▍   | 772/1195 [16:15<08:33,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.00it/s]\n",
      "Processing Dataset:  65%|██████▍   | 773/1195 [16:17<08:13,  1.17s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.56it/s]\n",
      "Processing Dataset:  65%|██████▍   | 774/1195 [16:18<08:16,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.72it/s]\n",
      "Processing Dataset:  65%|██████▍   | 775/1195 [16:19<08:17,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.55it/s]\n",
      "Processing Dataset:  65%|██████▍   | 776/1195 [16:20<08:01,  1.15s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 75.24it/s]\n",
      "Processing Dataset:  65%|██████▌   | 777/1195 [16:21<07:40,  1.10s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 23.27it/s]\n",
      "Processing Dataset:  65%|██████▌   | 778/1195 [16:22<07:47,  1.12s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 55.61it/s]\n",
      "Processing Dataset:  65%|██████▌   | 779/1195 [16:23<07:36,  1.10s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 72.07it/s]\n",
      "Processing Dataset:  65%|██████▌   | 780/1195 [16:25<08:18,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.43it/s]\n",
      "Processing Dataset:  65%|██████▌   | 781/1195 [16:26<08:15,  1.20s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 60.23it/s]\n",
      "Processing Dataset:  65%|██████▌   | 782/1195 [16:27<07:54,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 35.53it/s]\n",
      "Processing Dataset:  66%|██████▌   | 783/1195 [16:28<08:02,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.01it/s]\n",
      "Processing Dataset:  66%|██████▌   | 784/1195 [16:29<07:55,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 60.28it/s]\n",
      "Processing Dataset:  66%|██████▌   | 785/1195 [16:30<07:30,  1.10s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.96it/s]\n",
      "Processing Dataset:  66%|██████▌   | 786/1195 [16:31<07:46,  1.14s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.97it/s]\n",
      "Processing Dataset:  66%|██████▌   | 787/1195 [16:33<07:52,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.93it/s]\n",
      "Processing Dataset:  66%|██████▌   | 788/1195 [16:34<07:49,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 61.13it/s]\n",
      "Processing Dataset:  66%|██████▌   | 789/1195 [16:35<08:38,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 39.19it/s]\n",
      "Processing Dataset:  66%|██████▌   | 790/1195 [16:37<08:38,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 68.58it/s]\n",
      "Processing Dataset:  66%|██████▌   | 791/1195 [16:38<08:41,  1.29s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 80.50it/s]\n",
      "Processing Dataset:  66%|██████▋   | 792/1195 [16:39<08:51,  1.32s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.02it/s]\n",
      "Processing Dataset:  66%|██████▋   | 793/1195 [16:40<08:25,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.39it/s]\n",
      "Processing Dataset:  66%|██████▋   | 794/1195 [16:42<08:09,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.69it/s]\n",
      "Processing Dataset:  67%|██████▋   | 795/1195 [16:43<08:06,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.41it/s]\n",
      "Processing Dataset:  67%|██████▋   | 796/1195 [16:44<08:23,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.60it/s]\n",
      "Processing Dataset:  67%|██████▋   | 797/1195 [16:45<08:04,  1.22s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 61.95it/s]\n",
      "Processing Dataset:  67%|██████▋   | 798/1195 [16:47<08:10,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.11it/s]\n",
      "Processing Dataset:  67%|██████▋   | 799/1195 [16:48<08:14,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.96it/s]\n",
      "Processing Dataset:  67%|██████▋   | 800/1195 [16:49<08:11,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.03it/s]\n",
      "Processing Dataset:  67%|██████▋   | 801/1195 [16:50<08:02,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.27it/s]\n",
      "Processing Dataset:  67%|██████▋   | 802/1195 [16:51<08:02,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 38.30it/s]\n",
      "Processing Dataset:  67%|██████▋   | 803/1195 [16:53<07:55,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.96it/s]\n",
      "Processing Dataset:  67%|██████▋   | 804/1195 [16:54<07:37,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.82it/s]\n",
      "Processing Dataset:  67%|██████▋   | 805/1195 [16:55<07:44,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.30it/s]\n",
      "Processing Dataset:  67%|██████▋   | 806/1195 [16:56<07:42,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.39it/s]\n",
      "Processing Dataset:  68%|██████▊   | 807/1195 [16:57<07:43,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.10it/s]\n",
      "Processing Dataset:  68%|██████▊   | 808/1195 [16:59<07:40,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.73it/s]\n",
      "Processing Dataset:  68%|██████▊   | 809/1195 [17:00<07:52,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.27it/s]\n",
      "Processing Dataset:  68%|██████▊   | 810/1195 [17:01<07:34,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.45it/s]\n",
      "Processing Dataset:  68%|██████▊   | 811/1195 [17:02<07:37,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.06it/s]\n",
      "Processing Dataset:  68%|██████▊   | 812/1195 [17:03<07:40,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.14it/s]\n",
      "Processing Dataset:  68%|██████▊   | 813/1195 [17:12<22:01,  3.46s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 73.91it/s]\n",
      "Processing Dataset:  68%|██████▊   | 814/1195 [17:16<23:23,  3.68s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.57it/s]\n",
      "Processing Dataset:  68%|██████▊   | 815/1195 [17:18<18:41,  2.95s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.44it/s]\n",
      "Processing Dataset:  68%|██████▊   | 816/1195 [17:19<15:09,  2.40s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 83.79it/s]\n",
      "Processing Dataset:  68%|██████▊   | 817/1195 [17:20<12:58,  2.06s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.67it/s]\n",
      "Processing Dataset:  68%|██████▊   | 818/1195 [17:21<11:16,  1.80s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.09it/s]\n",
      "Processing Dataset:  69%|██████▊   | 819/1195 [17:22<09:57,  1.59s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.62it/s]\n",
      "Processing Dataset:  69%|██████▊   | 820/1195 [17:23<09:09,  1.46s/it]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Row 820: Skipping due to missing or invalid 'text' column.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.70it/s]\n",
      "Processing Dataset:  69%|██████▉   | 822/1195 [17:25<07:00,  1.13s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.30it/s]\n",
      "Processing Dataset:  69%|██████▉   | 823/1195 [17:26<07:30,  1.21s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 68.23it/s]\n",
      "Processing Dataset:  69%|██████▉   | 824/1195 [17:27<07:11,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.36it/s]\n",
      "Processing Dataset:  69%|██████▉   | 825/1195 [17:28<07:04,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 81.58it/s]\n",
      "Processing Dataset:  69%|██████▉   | 826/1195 [17:30<07:13,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.21it/s]\n",
      "Processing Dataset:  69%|██████▉   | 827/1195 [17:31<07:09,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 65.26it/s]\n",
      "Processing Dataset:  69%|██████▉   | 828/1195 [17:32<07:26,  1.22s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 93.78it/s]\n",
      "Processing Dataset:  69%|██████▉   | 829/1195 [17:33<07:24,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.86it/s]\n",
      "Processing Dataset:  69%|██████▉   | 830/1195 [17:35<07:16,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.92it/s]\n",
      "Processing Dataset:  70%|██████▉   | 831/1195 [17:36<07:19,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.80it/s]\n",
      "Processing Dataset:  70%|██████▉   | 832/1195 [17:37<07:16,  1.20s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 81.81it/s]\n",
      "Processing Dataset:  70%|██████▉   | 833/1195 [17:38<07:05,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.48it/s]\n",
      "Processing Dataset:  70%|██████▉   | 834/1195 [17:39<07:02,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.96it/s]\n",
      "Processing Dataset:  70%|██████▉   | 835/1195 [17:41<07:11,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.00it/s]\n",
      "Processing Dataset:  70%|██████▉   | 836/1195 [17:42<06:58,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.23it/s]\n",
      "Processing Dataset:  70%|███████   | 837/1195 [17:43<06:51,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.61it/s]\n",
      "Processing Dataset:  70%|███████   | 838/1195 [17:44<06:47,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.61it/s]\n",
      "Processing Dataset:  70%|███████   | 839/1195 [17:45<06:57,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.62it/s]\n",
      "Processing Dataset:  70%|███████   | 840/1195 [17:46<07:13,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.34it/s]\n",
      "Processing Dataset:  70%|███████   | 841/1195 [17:48<07:11,  1.22s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 19.00it/s]\n",
      "Processing Dataset:  70%|███████   | 842/1195 [17:49<06:42,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 32.87it/s]\n",
      "Processing Dataset:  71%|███████   | 843/1195 [17:50<07:03,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.94it/s]\n",
      "Processing Dataset:  71%|███████   | 844/1195 [17:51<06:58,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.45it/s]\n",
      "Processing Dataset:  71%|███████   | 845/1195 [17:52<06:49,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.17it/s]\n",
      "Processing Dataset:  71%|███████   | 846/1195 [17:53<06:43,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.03it/s]\n",
      "Processing Dataset:  71%|███████   | 847/1195 [17:55<06:45,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.86it/s]\n",
      "Processing Dataset:  71%|███████   | 848/1195 [17:56<06:30,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.08it/s]\n",
      "Processing Dataset:  71%|███████   | 849/1195 [17:57<06:45,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.25it/s]\n",
      "Processing Dataset:  71%|███████   | 850/1195 [17:58<06:39,  1.16s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 65.45it/s]\n",
      "Processing Dataset:  71%|███████   | 851/1195 [17:59<06:23,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 54.98it/s]\n",
      "Processing Dataset:  71%|███████▏  | 852/1195 [18:00<06:31,  1.14s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 51.56it/s]\n",
      "Processing Dataset:  71%|███████▏  | 853/1195 [18:01<06:14,  1.10s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.56it/s]\n",
      "Processing Dataset:  71%|███████▏  | 854/1195 [18:03<06:46,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.16it/s]\n",
      "Processing Dataset:  72%|███████▏  | 855/1195 [18:04<06:49,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.64it/s]\n",
      "Processing Dataset:  72%|███████▏  | 856/1195 [18:05<06:32,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.37it/s]\n",
      "Processing Dataset:  72%|███████▏  | 857/1195 [18:06<06:33,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.11it/s]\n",
      "Processing Dataset:  72%|███████▏  | 858/1195 [18:07<06:15,  1.11s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 93.31it/s]\n",
      "Processing Dataset:  72%|███████▏  | 859/1195 [18:08<06:33,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 63.81it/s]\n",
      "Processing Dataset:  72%|███████▏  | 860/1195 [18:10<06:32,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.46it/s]\n",
      "Processing Dataset:  72%|███████▏  | 861/1195 [18:10<06:12,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.43it/s]\n",
      "Processing Dataset:  72%|███████▏  | 862/1195 [18:12<06:27,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 70.98it/s]\n",
      "Processing Dataset:  72%|███████▏  | 863/1195 [18:13<06:50,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.45it/s]\n",
      "Processing Dataset:  72%|███████▏  | 864/1195 [18:14<06:49,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 84.56it/s]\n",
      "Processing Dataset:  72%|███████▏  | 865/1195 [18:16<06:41,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.43it/s]\n",
      "Processing Dataset:  72%|███████▏  | 866/1195 [18:17<06:32,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.54it/s]\n",
      "Processing Dataset:  73%|███████▎  | 867/1195 [18:18<06:23,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.00it/s]\n",
      "Processing Dataset:  73%|███████▎  | 868/1195 [18:19<06:24,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.50it/s]\n",
      "Processing Dataset:  73%|███████▎  | 869/1195 [18:20<06:20,  1.17s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 75.90it/s]\n",
      "Processing Dataset:  73%|███████▎  | 870/1195 [18:21<05:58,  1.10s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.00it/s]\n",
      "Processing Dataset:  73%|███████▎  | 871/1195 [18:22<06:05,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 89.05it/s]\n",
      "Processing Dataset:  73%|███████▎  | 872/1195 [18:24<06:26,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 34.33it/s]\n",
      "Processing Dataset:  73%|███████▎  | 873/1195 [18:25<06:22,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.37it/s]\n",
      "Processing Dataset:  73%|███████▎  | 874/1195 [18:26<06:17,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.01it/s]\n",
      "Processing Dataset:  73%|███████▎  | 875/1195 [18:27<06:10,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.96it/s]\n",
      "Processing Dataset:  73%|███████▎  | 876/1195 [18:28<06:08,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 77.72it/s]\n",
      "Processing Dataset:  73%|███████▎  | 877/1195 [18:30<06:25,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.80it/s]\n",
      "Processing Dataset:  73%|███████▎  | 878/1195 [18:31<06:17,  1.19s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.71it/s]\n",
      "Processing Dataset:  74%|███████▎  | 879/1195 [18:32<06:43,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.35it/s]\n",
      "Processing Dataset:  74%|███████▎  | 880/1195 [18:34<06:46,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.62it/s]\n",
      "Processing Dataset:  74%|███████▎  | 881/1195 [18:35<06:31,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.10it/s]\n",
      "Processing Dataset:  74%|███████▍  | 882/1195 [18:36<06:09,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.18it/s]\n",
      "Processing Dataset:  74%|███████▍  | 883/1195 [18:37<05:58,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.75it/s]\n",
      "Processing Dataset:  74%|███████▍  | 884/1195 [18:38<06:20,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 75.66it/s]\n",
      "Processing Dataset:  74%|███████▍  | 885/1195 [18:40<06:44,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.14it/s]\n",
      "Processing Dataset:  74%|███████▍  | 886/1195 [18:41<06:43,  1.31s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 85.43it/s]\n",
      "Processing Dataset:  74%|███████▍  | 887/1195 [18:42<06:25,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.85it/s]\n",
      "Processing Dataset:  74%|███████▍  | 888/1195 [18:43<06:14,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 76.67it/s]\n",
      "Processing Dataset:  74%|███████▍  | 889/1195 [18:44<06:06,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.41it/s]\n",
      "Processing Dataset:  74%|███████▍  | 890/1195 [18:46<06:10,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.62it/s]\n",
      "Processing Dataset:  75%|███████▍  | 891/1195 [18:47<06:22,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.95it/s]\n",
      "Processing Dataset:  75%|███████▍  | 892/1195 [18:48<06:30,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 50.61it/s]\n",
      "Processing Dataset:  75%|███████▍  | 893/1195 [18:50<06:46,  1.35s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.07it/s]\n",
      "Processing Dataset:  75%|███████▍  | 894/1195 [18:51<06:42,  1.34s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.15it/s]\n",
      "Processing Dataset:  75%|███████▍  | 895/1195 [18:52<06:19,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.15it/s]\n",
      "Processing Dataset:  75%|███████▍  | 896/1195 [18:53<06:10,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.43it/s]\n",
      "Processing Dataset:  75%|███████▌  | 897/1195 [18:55<05:57,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.09it/s]\n",
      "Processing Dataset:  75%|███████▌  | 898/1195 [18:56<06:02,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.19it/s]\n",
      "Processing Dataset:  75%|███████▌  | 899/1195 [18:57<06:12,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.61it/s]\n",
      "Processing Dataset:  75%|███████▌  | 900/1195 [18:58<06:03,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.95it/s]\n",
      "Processing Dataset:  75%|███████▌  | 901/1195 [19:00<05:59,  1.22s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 81.33it/s]\n",
      "Processing Dataset:  75%|███████▌  | 902/1195 [19:01<06:08,  1.26s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 66.65it/s]\n",
      "Processing Dataset:  76%|███████▌  | 903/1195 [19:02<06:08,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.97it/s]\n",
      "Processing Dataset:  76%|███████▌  | 904/1195 [19:04<06:15,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 63.57it/s]\n",
      "Processing Dataset:  76%|███████▌  | 905/1195 [19:05<05:58,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.15it/s]\n",
      "Processing Dataset:  76%|███████▌  | 906/1195 [19:06<06:01,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.23it/s]\n",
      "Processing Dataset:  76%|███████▌  | 907/1195 [19:07<06:01,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 93.90it/s]\n",
      "Processing Dataset:  76%|███████▌  | 908/1195 [19:08<06:05,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.55it/s]\n",
      "Processing Dataset:  76%|███████▌  | 909/1195 [19:10<05:46,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.94it/s]\n",
      "Processing Dataset:  76%|███████▌  | 910/1195 [19:11<05:30,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 84.13it/s]\n",
      "Processing Dataset:  76%|███████▌  | 911/1195 [19:12<05:17,  1.12s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 86.70it/s]\n",
      "Processing Dataset:  76%|███████▋  | 912/1195 [19:13<05:36,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 63.84it/s]\n",
      "Processing Dataset:  76%|███████▋  | 913/1195 [19:14<05:49,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.32it/s]\n",
      "Processing Dataset:  76%|███████▋  | 914/1195 [19:15<05:35,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.07it/s]\n",
      "Processing Dataset:  77%|███████▋  | 915/1195 [19:16<05:25,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.00it/s]\n",
      "Processing Dataset:  77%|███████▋  | 916/1195 [19:18<05:35,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.05it/s]\n",
      "Processing Dataset:  77%|███████▋  | 917/1195 [19:19<05:20,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.82it/s]\n",
      "Processing Dataset:  77%|███████▋  | 918/1195 [19:20<05:14,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.85it/s]\n",
      "Processing Dataset:  77%|███████▋  | 919/1195 [19:21<05:15,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.75it/s]\n",
      "Processing Dataset:  77%|███████▋  | 920/1195 [19:22<05:21,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.91it/s]\n",
      "Processing Dataset:  77%|███████▋  | 921/1195 [19:24<05:28,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 86.52it/s]\n",
      "Processing Dataset:  77%|███████▋  | 922/1195 [19:25<05:25,  1.19s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 38.77it/s]\n",
      "Processing Dataset:  77%|███████▋  | 923/1195 [19:26<05:11,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.01it/s]\n",
      "Processing Dataset:  77%|███████▋  | 924/1195 [19:27<05:12,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.49it/s]\n",
      "Processing Dataset:  77%|███████▋  | 925/1195 [19:28<05:15,  1.17s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 90.65it/s]\n",
      "Processing Dataset:  77%|███████▋  | 926/1195 [19:29<05:18,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.81it/s]\n",
      "Processing Dataset:  78%|███████▊  | 927/1195 [19:31<05:29,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.79it/s]\n",
      "Processing Dataset:  78%|███████▊  | 928/1195 [19:32<05:14,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.00it/s]\n",
      "Processing Dataset:  78%|███████▊  | 929/1195 [19:33<05:15,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.88it/s]\n",
      "Processing Dataset:  78%|███████▊  | 930/1195 [19:34<05:23,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.97it/s]\n",
      "Processing Dataset:  78%|███████▊  | 931/1195 [19:35<05:07,  1.16s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.37it/s]\n",
      "Processing Dataset:  78%|███████▊  | 932/1195 [19:37<05:15,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.18it/s]\n",
      "Processing Dataset:  78%|███████▊  | 933/1195 [19:38<05:10,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 31.44it/s]\n",
      "Processing Dataset:  78%|███████▊  | 934/1195 [19:39<05:10,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.15it/s]\n",
      "Processing Dataset:  78%|███████▊  | 935/1195 [19:40<05:18,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.02it/s]\n",
      "Processing Dataset:  78%|███████▊  | 936/1195 [19:41<05:07,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.14it/s]\n",
      "Processing Dataset:  78%|███████▊  | 937/1195 [19:42<04:57,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 82.26it/s]\n",
      "Processing Dataset:  78%|███████▊  | 938/1195 [19:44<05:08,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.88it/s]\n",
      "Processing Dataset:  79%|███████▊  | 939/1195 [19:45<05:19,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.57it/s]\n",
      "Processing Dataset:  79%|███████▊  | 940/1195 [19:47<05:28,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.04it/s]\n",
      "Processing Dataset:  79%|███████▊  | 941/1195 [19:48<05:18,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 49.83it/s]\n",
      "Processing Dataset:  79%|███████▉  | 942/1195 [19:49<05:03,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 94.19it/s]\n",
      "Processing Dataset:  79%|███████▉  | 943/1195 [19:50<05:00,  1.19s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 22.76it/s]\n",
      "Processing Dataset:  79%|███████▉  | 944/1195 [19:51<04:55,  1.18s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.91it/s]\n",
      "Processing Dataset:  79%|███████▉  | 945/1195 [19:53<05:34,  1.34s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 68.32it/s]\n",
      "Processing Dataset:  79%|███████▉  | 946/1195 [19:54<05:29,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.86it/s]\n",
      "Processing Dataset:  79%|███████▉  | 947/1195 [19:55<05:04,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.21it/s]\n",
      "Processing Dataset:  79%|███████▉  | 948/1195 [19:56<05:05,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 90.87it/s]\n",
      "Processing Dataset:  79%|███████▉  | 949/1195 [19:57<04:53,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.75it/s]\n",
      "Processing Dataset:  79%|███████▉  | 950/1195 [19:59<04:49,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.54it/s]\n",
      "Processing Dataset:  80%|███████▉  | 951/1195 [20:00<04:50,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.36it/s]\n",
      "Processing Dataset:  80%|███████▉  | 952/1195 [20:01<04:43,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.57it/s]\n",
      "Processing Dataset:  80%|███████▉  | 953/1195 [20:02<04:40,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.68it/s]\n",
      "Processing Dataset:  80%|███████▉  | 954/1195 [20:03<04:54,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.48it/s]\n",
      "Processing Dataset:  80%|███████▉  | 955/1195 [20:05<05:07,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.05it/s]\n",
      "Processing Dataset:  80%|████████  | 956/1195 [20:06<04:58,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.36it/s]\n",
      "Processing Dataset:  80%|████████  | 957/1195 [20:07<04:54,  1.24s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 94.54it/s]\n",
      "Processing Dataset:  80%|████████  | 958/1195 [20:09<04:59,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.70it/s]\n",
      "Processing Dataset:  80%|████████  | 959/1195 [20:10<04:55,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.01it/s]\n",
      "Processing Dataset:  80%|████████  | 960/1195 [20:11<04:42,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.58it/s]\n",
      "Processing Dataset:  80%|████████  | 961/1195 [20:12<04:38,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 87.64it/s]\n",
      "Processing Dataset:  81%|████████  | 962/1195 [20:13<04:36,  1.19s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 87.79it/s]\n",
      "Processing Dataset:  81%|████████  | 963/1195 [20:14<04:37,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.29it/s]\n",
      "Processing Dataset:  81%|████████  | 964/1195 [20:16<04:42,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.82it/s]\n",
      "Processing Dataset:  81%|████████  | 965/1195 [20:17<04:42,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 78.33it/s]\n",
      "Processing Dataset:  81%|████████  | 966/1195 [20:18<04:44,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.53it/s]\n",
      "Processing Dataset:  81%|████████  | 967/1195 [20:19<04:46,  1.25s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.98it/s]\n",
      "Processing Dataset:  81%|████████  | 968/1195 [20:21<04:42,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 66.22it/s]\n",
      "Processing Dataset:  81%|████████  | 969/1195 [20:22<04:32,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.47it/s]\n",
      "Processing Dataset:  81%|████████  | 970/1195 [20:23<04:31,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.84it/s]\n",
      "Processing Dataset:  81%|████████▏ | 971/1195 [20:24<04:33,  1.22s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.25it/s]\n",
      "Processing Dataset:  81%|████████▏ | 972/1195 [20:25<04:22,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 48.09it/s]\n",
      "Processing Dataset:  81%|████████▏ | 973/1195 [20:27<04:26,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 54.86it/s]\n",
      "Processing Dataset:  82%|████████▏ | 974/1195 [20:28<04:42,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.33it/s]\n",
      "Processing Dataset:  82%|████████▏ | 975/1195 [20:29<04:48,  1.31s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.34it/s]\n",
      "Processing Dataset:  82%|████████▏ | 976/1195 [20:31<05:00,  1.37s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.91it/s]\n",
      "Processing Dataset:  82%|████████▏ | 977/1195 [20:32<04:27,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 47.90it/s]\n",
      "Processing Dataset:  82%|████████▏ | 978/1195 [20:33<04:27,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.20it/s]\n",
      "Processing Dataset:  82%|████████▏ | 979/1195 [20:34<04:24,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 87.37it/s]\n",
      "Processing Dataset:  82%|████████▏ | 980/1195 [20:35<04:13,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.46it/s]\n",
      "Processing Dataset:  82%|████████▏ | 981/1195 [20:37<04:16,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.23it/s]\n",
      "Processing Dataset:  82%|████████▏ | 982/1195 [20:38<04:14,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.29it/s]\n",
      "Processing Dataset:  82%|████████▏ | 983/1195 [20:39<04:08,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.10it/s]\n",
      "Processing Dataset:  82%|████████▏ | 984/1195 [20:40<04:19,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.97it/s]\n",
      "Processing Dataset:  82%|████████▏ | 985/1195 [20:42<04:31,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.61it/s]\n",
      "Processing Dataset:  83%|████████▎ | 986/1195 [20:43<04:25,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.14it/s]\n",
      "Processing Dataset:  83%|████████▎ | 987/1195 [20:44<04:15,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.25it/s]\n",
      "Processing Dataset:  83%|████████▎ | 988/1195 [20:45<04:11,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 60.79it/s]\n",
      "Processing Dataset:  83%|████████▎ | 989/1195 [20:46<04:03,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 83.91it/s]\n",
      "Processing Dataset:  83%|████████▎ | 990/1195 [20:48<04:10,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.16it/s]\n",
      "Processing Dataset:  83%|████████▎ | 991/1195 [20:49<03:59,  1.17s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 64.73it/s]\n",
      "Processing Dataset:  83%|████████▎ | 992/1195 [20:50<03:50,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.77it/s]\n",
      "Processing Dataset:  83%|████████▎ | 993/1195 [20:51<03:47,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.29it/s]\n",
      "Processing Dataset:  83%|████████▎ | 994/1195 [20:52<04:11,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.51it/s]\n",
      "Processing Dataset:  83%|████████▎ | 995/1195 [20:54<04:14,  1.27s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.82it/s]\n",
      "Processing Dataset:  83%|████████▎ | 996/1195 [20:55<04:13,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.10it/s]\n",
      "Processing Dataset:  83%|████████▎ | 997/1195 [20:56<04:02,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.55it/s]\n",
      "Processing Dataset:  84%|████████▎ | 998/1195 [20:57<03:59,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.88it/s]\n",
      "Processing Dataset:  84%|████████▎ | 999/1195 [20:58<03:50,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.81it/s]\n",
      "Processing Dataset:  84%|████████▎ | 1000/1195 [21:00<03:51,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.73it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1001/1195 [21:01<03:49,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 86.23it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1002/1195 [21:02<03:40,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.39it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1003/1195 [21:03<03:37,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 61.41it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1004/1195 [21:04<03:48,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 43.45it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1005/1195 [21:06<03:49,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.37it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1006/1195 [21:07<03:45,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.67it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1007/1195 [21:08<03:41,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 91.96it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1008/1195 [21:09<03:42,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.51it/s]\n",
      "Processing Dataset:  84%|████████▍ | 1009/1195 [21:10<03:53,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.27it/s]\n",
      "Processing Dataset:  85%|████████▍ | 1010/1195 [21:11<03:32,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.48it/s]\n",
      "Processing Dataset:  85%|████████▍ | 1011/1195 [21:12<03:26,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.77it/s]\n",
      "Processing Dataset:  85%|████████▍ | 1012/1195 [21:14<03:22,  1.11s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.68it/s]\n",
      "Processing Dataset:  85%|████████▍ | 1013/1195 [21:15<03:22,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.69it/s]\n",
      "Processing Dataset:  85%|████████▍ | 1014/1195 [21:16<03:29,  1.16s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 64.87it/s]\n",
      "Processing Dataset:  85%|████████▍ | 1015/1195 [21:17<03:29,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.02it/s]\n",
      "Processing Dataset:  85%|████████▌ | 1016/1195 [21:18<03:33,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.63it/s]\n",
      "Processing Dataset:  85%|████████▌ | 1017/1195 [21:20<03:33,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.10it/s]\n",
      "Processing Dataset:  85%|████████▌ | 1018/1195 [21:21<03:33,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.51it/s]\n",
      "Processing Dataset:  85%|████████▌ | 1019/1195 [21:22<03:29,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.98it/s]\n",
      "Processing Dataset:  85%|████████▌ | 1020/1195 [21:23<03:21,  1.15s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.16it/s]\n",
      "Processing Dataset:  85%|████████▌ | 1021/1195 [21:24<03:26,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.88it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1022/1195 [21:25<03:22,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.64it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1023/1195 [21:27<03:23,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.52it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1024/1195 [21:28<03:24,  1.20s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 55.61it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1025/1195 [21:29<03:40,  1.29s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.42it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1026/1195 [21:30<03:29,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.21it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1027/1195 [21:32<03:25,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.88it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1028/1195 [21:33<03:17,  1.19s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 59.10it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1029/1195 [21:34<03:06,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.81it/s]\n",
      "Processing Dataset:  86%|████████▌ | 1030/1195 [21:35<03:14,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.59it/s]\n",
      "Processing Dataset:  86%|████████▋ | 1031/1195 [21:36<03:21,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.94it/s]\n",
      "Processing Dataset:  86%|████████▋ | 1032/1195 [21:37<03:09,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.97it/s]\n",
      "Processing Dataset:  86%|████████▋ | 1033/1195 [21:39<03:16,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.86it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1034/1195 [21:40<03:14,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.05it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1035/1195 [21:41<03:15,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 61.22it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1036/1195 [21:42<03:16,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.03it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1037/1195 [21:44<03:19,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.00it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1038/1195 [21:45<03:14,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.39it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1039/1195 [21:46<03:10,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.57it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1040/1195 [21:47<03:05,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.92it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1041/1195 [21:49<03:10,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 65.72it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1042/1195 [21:50<03:05,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.40it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1043/1195 [21:51<03:04,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 83.79it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1044/1195 [21:52<02:59,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.52it/s]\n",
      "Processing Dataset:  87%|████████▋ | 1045/1195 [21:54<03:08,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 76.41it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1046/1195 [21:55<03:18,  1.33s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.83it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1047/1195 [21:56<03:15,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.09it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1048/1195 [21:58<03:10,  1.30s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 69.35it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1049/1195 [21:59<03:01,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.79it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1050/1195 [22:00<02:53,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.25it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1051/1195 [22:01<02:44,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.15it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1052/1195 [22:02<02:49,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.77it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1053/1195 [22:03<02:48,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 61.62it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1054/1195 [22:04<02:45,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.55it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1055/1195 [22:06<02:46,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 85.04it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1056/1195 [22:07<02:57,  1.28s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.03it/s]\n",
      "Processing Dataset:  88%|████████▊ | 1057/1195 [22:08<02:58,  1.29s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.64it/s]\n",
      "Processing Dataset:  89%|████████▊ | 1058/1195 [22:09<02:46,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.34it/s]\n",
      "Processing Dataset:  89%|████████▊ | 1059/1195 [22:11<02:45,  1.21s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.82it/s]\n",
      "Processing Dataset:  89%|████████▊ | 1060/1195 [22:12<02:50,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.61it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1061/1195 [22:13<02:46,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.64it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1062/1195 [22:15<02:48,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.90it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1063/1195 [22:16<02:45,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 25.65it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1064/1195 [22:17<02:42,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 65.01it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1065/1195 [22:18<02:42,  1.25s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 28.35it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1066/1195 [22:19<02:33,  1.19s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.08it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1067/1195 [22:21<02:37,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.68it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1068/1195 [22:22<02:35,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.75it/s]\n",
      "Processing Dataset:  89%|████████▉ | 1069/1195 [22:23<02:41,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.50it/s]\n",
      "Processing Dataset:  90%|████████▉ | 1070/1195 [22:24<02:33,  1.23s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 79.92it/s]\n",
      "Processing Dataset:  90%|████████▉ | 1071/1195 [22:26<02:35,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.41it/s]\n",
      "Processing Dataset:  90%|████████▉ | 1072/1195 [22:27<02:34,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.21it/s]\n",
      "Processing Dataset:  90%|████████▉ | 1073/1195 [22:28<02:28,  1.21s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 74.25it/s]\n",
      "Processing Dataset:  90%|████████▉ | 1074/1195 [22:29<02:28,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 36.21it/s]\n",
      "Processing Dataset:  90%|████████▉ | 1075/1195 [22:31<02:28,  1.24s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.66it/s]\n",
      "Processing Dataset:  90%|█████████ | 1076/1195 [22:32<02:22,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.82it/s]\n",
      "Processing Dataset:  90%|█████████ | 1077/1195 [22:33<02:20,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.33it/s]\n",
      "Processing Dataset:  90%|█████████ | 1078/1195 [22:34<02:19,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.95it/s]\n",
      "Processing Dataset:  90%|█████████ | 1079/1195 [22:35<02:15,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.27it/s]\n",
      "Processing Dataset:  90%|█████████ | 1080/1195 [22:36<02:10,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.14it/s]\n",
      "Processing Dataset:  90%|█████████ | 1081/1195 [22:37<02:07,  1.12s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 86.27it/s]\n",
      "Processing Dataset:  91%|█████████ | 1082/1195 [22:39<02:13,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 84.28it/s]\n",
      "Processing Dataset:  91%|█████████ | 1083/1195 [22:40<02:06,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.16it/s]\n",
      "Processing Dataset:  91%|█████████ | 1084/1195 [22:41<02:05,  1.13s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.87it/s]\n",
      "Processing Dataset:  91%|█████████ | 1085/1195 [22:42<02:08,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.76it/s]\n",
      "Processing Dataset:  91%|█████████ | 1086/1195 [22:43<02:14,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.82it/s]\n",
      "Processing Dataset:  91%|█████████ | 1087/1195 [22:45<02:15,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.20it/s]\n",
      "Processing Dataset:  91%|█████████ | 1088/1195 [22:46<02:07,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.44it/s]\n",
      "Processing Dataset:  91%|█████████ | 1089/1195 [22:52<04:49,  2.73s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.83it/s]\n",
      "Processing Dataset:  91%|█████████ | 1090/1195 [22:53<04:03,  2.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.03it/s]\n",
      "Processing Dataset:  91%|█████████▏| 1091/1195 [22:55<03:34,  2.06s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 68.98it/s]\n",
      "Processing Dataset:  91%|█████████▏| 1092/1195 [22:56<03:00,  1.75s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 63.82it/s]\n",
      "Processing Dataset:  91%|█████████▏| 1093/1195 [22:57<02:37,  1.54s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 77.87it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1094/1195 [22:58<02:29,  1.48s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.62it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1095/1195 [22:59<02:15,  1.35s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 59.02it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1096/1195 [23:01<02:10,  1.32s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.39it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1097/1195 [23:02<02:02,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 74.37it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1098/1195 [23:03<01:57,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.45it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1099/1195 [23:04<01:54,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.39it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1100/1195 [23:05<01:55,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 62.45it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1101/1195 [23:07<01:58,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 40.23it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1102/1195 [23:08<01:55,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 84.44it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1103/1195 [23:09<01:50,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.00it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1104/1195 [23:10<01:45,  1.16s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 70.90it/s]\n",
      "Processing Dataset:  92%|█████████▏| 1105/1195 [23:11<01:41,  1.13s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 77.63it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1106/1195 [23:13<01:49,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.30it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1107/1195 [23:14<01:45,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.82it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1108/1195 [23:15<01:42,  1.18s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 63.63it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1109/1195 [23:16<01:43,  1.20s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.11it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1110/1195 [23:17<01:45,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.08it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1111/1195 [23:19<01:46,  1.27s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 51.31it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1112/1195 [23:20<01:44,  1.26s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.76it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1113/1195 [23:21<01:40,  1.23s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.95it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1114/1195 [23:22<01:35,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 63.13it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1115/1195 [23:23<01:33,  1.17s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 70.05it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1116/1195 [23:24<01:30,  1.15s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 84.81it/s]\n",
      "Processing Dataset:  93%|█████████▎| 1117/1195 [23:26<01:32,  1.19s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 58.41it/s]\n",
      "Processing Dataset:  94%|█████████▎| 1118/1195 [23:27<01:29,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.23it/s]\n",
      "Processing Dataset:  94%|█████████▎| 1119/1195 [23:28<01:28,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.32it/s]\n",
      "Processing Dataset:  94%|█████████▎| 1120/1195 [23:29<01:31,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.17it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1121/1195 [23:31<01:31,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.92it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1122/1195 [23:32<01:36,  1.32s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.99it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1123/1195 [23:33<01:32,  1.28s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.09it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1124/1195 [23:34<01:25,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 61.93it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1125/1195 [23:36<01:24,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.71it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1126/1195 [23:37<01:20,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.49it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1127/1195 [23:38<01:17,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 90.60it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1128/1195 [23:39<01:18,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.06it/s]\n",
      "Processing Dataset:  94%|█████████▍| 1129/1195 [23:40<01:16,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 71.42it/s]\n",
      "Processing Dataset:  95%|█████████▍| 1130/1195 [23:41<01:14,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.50it/s]\n",
      "Processing Dataset:  95%|█████████▍| 1131/1195 [23:42<01:15,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.39it/s]\n",
      "Processing Dataset:  95%|█████████▍| 1132/1195 [23:44<01:16,  1.21s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.24it/s]\n",
      "Processing Dataset:  95%|█████████▍| 1133/1195 [23:45<01:15,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.24it/s]\n",
      "Processing Dataset:  95%|█████████▍| 1134/1195 [23:46<01:14,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 79.21it/s]\n",
      "Processing Dataset:  95%|█████████▍| 1135/1195 [23:48<01:15,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.97it/s]\n",
      "Processing Dataset:  95%|█████████▌| 1136/1195 [23:49<01:12,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.19it/s]\n",
      "Processing Dataset:  95%|█████████▌| 1137/1195 [23:50<01:13,  1.26s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.26it/s]\n",
      "Processing Dataset:  95%|█████████▌| 1138/1195 [23:51<01:07,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 45.28it/s]\n",
      "Processing Dataset:  95%|█████████▌| 1139/1195 [23:52<01:06,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.86it/s]\n",
      "Processing Dataset:  95%|█████████▌| 1140/1195 [23:54<01:06,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 61.53it/s]\n",
      "Processing Dataset:  95%|█████████▌| 1141/1195 [23:55<01:03,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 58.44it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1142/1195 [23:56<01:05,  1.23s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.50it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1143/1195 [23:57<01:01,  1.17s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 69.11it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1144/1195 [23:58<00:57,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 72.94it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1145/1195 [23:59<00:56,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.89it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1146/1195 [24:01<00:58,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.53it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1147/1195 [24:02<00:55,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.22it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1148/1195 [24:03<00:54,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.90it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1149/1195 [24:04<00:51,  1.13s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.63it/s]\n",
      "Processing Dataset:  96%|█████████▌| 1150/1195 [24:05<00:49,  1.10s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.05it/s]\n",
      "Processing Dataset:  96%|█████████▋| 1151/1195 [24:06<00:49,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 55.60it/s]\n",
      "Processing Dataset:  96%|█████████▋| 1152/1195 [24:07<00:48,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 35.84it/s]\n",
      "Processing Dataset:  96%|█████████▋| 1153/1195 [24:08<00:46,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.24it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1154/1195 [24:09<00:45,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.11it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1155/1195 [24:11<00:46,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.80it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1156/1195 [24:12<00:44,  1.14s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.30it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1157/1195 [24:13<00:43,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.63it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1158/1195 [24:14<00:43,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.92it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1159/1195 [24:15<00:43,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.52it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1160/1195 [24:17<00:41,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 66.95it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1161/1195 [24:18<00:39,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.31it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1162/1195 [24:19<00:37,  1.15s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 53.44it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1163/1195 [24:20<00:38,  1.22s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.00it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1164/1195 [24:21<00:38,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.72it/s]\n",
      "Processing Dataset:  97%|█████████▋| 1165/1195 [24:23<00:36,  1.21s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 54.25it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1166/1195 [24:24<00:35,  1.22s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 67.93it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1167/1195 [24:25<00:32,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 64.42it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1168/1195 [24:26<00:30,  1.11s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.68it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1169/1195 [24:27<00:28,  1.11s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.92it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1170/1195 [24:28<00:27,  1.12s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.40it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1171/1195 [24:29<00:25,  1.08s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.61it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1172/1195 [24:30<00:24,  1.08s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.02it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1173/1195 [24:31<00:24,  1.13s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.19it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1174/1195 [24:33<00:23,  1.14s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 68.28it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1175/1195 [24:34<00:23,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 76.86it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1176/1195 [24:35<00:21,  1.16s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 67.16it/s]\n",
      "Processing Dataset:  98%|█████████▊| 1177/1195 [24:36<00:21,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.44it/s]\n",
      "Processing Dataset:  99%|█████████▊| 1178/1195 [24:38<00:21,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 89.41it/s]\n",
      "Processing Dataset:  99%|█████████▊| 1179/1195 [24:39<00:19,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.41it/s]\n",
      "Processing Dataset:  99%|█████████▊| 1180/1195 [24:40<00:17,  1.20s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 78.37it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1181/1195 [24:41<00:16,  1.16s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 65.90it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1182/1195 [24:42<00:15,  1.18s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 46.17it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1183/1195 [24:43<00:14,  1.18s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 52.72it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1184/1195 [24:44<00:12,  1.15s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 3/3 [00:00<00:00, 71.84it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1185/1195 [24:46<00:12,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 74.97it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1186/1195 [24:47<00:10,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.03it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1187/1195 [24:48<00:09,  1.17s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 69.54it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1188/1195 [24:49<00:08,  1.19s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 80.41it/s]\n",
      "Processing Dataset:  99%|█████████▉| 1189/1195 [24:51<00:07,  1.24s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 81.16it/s]\n",
      "Processing Dataset: 100%|█████████▉| 1190/1195 [24:52<00:06,  1.21s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.21it/s]\n",
      "Processing Dataset: 100%|█████████▉| 1191/1195 [24:53<00:04,  1.22s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 75.88it/s]\n",
      "Processing Dataset: 100%|█████████▉| 1192/1195 [24:54<00:03,  1.19s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 77.70it/s]\n",
      "Processing Dataset: 100%|█████████▉| 1193/1195 [24:56<00:02,  1.25s/it]\n",
      "100%|██████████| 2/2 [00:00<00:00, 50.11it/s]\n",
      "Processing Dataset: 100%|█████████▉| 1194/1195 [24:57<00:01,  1.25s/it]/usr/local/lib/python3.11/dist-packages/pyloudnorm/normalize.py:62: UserWarning: Possible clipped samples in output.\n",
      "  warnings.warn(\"Possible clipped samples in output.\")\n",
      "\n",
      "100%|██████████| 2/2 [00:00<00:00, 73.14it/s]\n",
      "Processing Dataset: 100%|██████████| 1195/1195 [24:58<00:00,  1.25s/it]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Dataset processing finished. Processed: 1194, Skipped: 1\n",
      "Moving Whisper model to CPU\n"
     ]
    }
   ],
   "source": [
    "#@title Tokenization Function\n",
    "\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "import io\n",
    "import tempfile\n",
    "from datasets import Dataset\n",
    "import sys\n",
    "sys.path.append('OuteTTS')\n",
    "import os\n",
    "import dac\n",
    "# V3 Imports\n",
    "from outetts.version.v3.audio_processor import AudioProcessor\n",
    "from outetts.version.v3.prompt_processor import PromptProcessor\n",
    "from outetts.dac.interface import DacInterface\n",
    "from outetts.models.config import ModelConfig # Need a dummy config for AudioProcessor\n",
    "import whisper\n",
    "from outetts.utils.preprocessing import text_normalizations\n",
    "import soundfile as sf\n",
    "import numpy as np\n",
    "\n",
    "class DataCreationV3:\n",
    "    def __init__(\n",
    "            self,\n",
    "            model_tokenizer_path: str,\n",
    "            whisper_model_name: str = \"turbo\",\n",
    "            device: str = None\n",
    "        ):\n",
    "\n",
    "        self.device = device if device else (\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        print(f\"Using device: {self.device}\")\n",
    "\n",
    "        # Create a dummy ModelConfig mainly for device and paths needed by AudioProcessor/DacInterface\n",
    "        dummy_config = ModelConfig(\n",
    "            tokenizer_path=model_tokenizer_path,\n",
    "            device=self.device,\n",
    "            audio_codec_path=None # Let AudioProcessor use default DAC path\n",
    "        )\n",
    "        self.audio_processor = AudioProcessor(config=dummy_config)\n",
    "        self.prompt_processor = PromptProcessor(model_tokenizer_path)\n",
    "\n",
    "        print(f\"Loading Whisper model: {whisper_model_name} on {self.device}\")\n",
    "        self.whisper_model = whisper.load_model(whisper_model_name, device=self.device)\n",
    "        print(\"Whisper model loaded.\")\n",
    "\n",
    "    # Renamed and adapted from the previous version\n",
    "    def create_speaker_representation(self, audio_bytes: bytes, transcript: str):\n",
    "        \"\"\"\n",
    "        Creates a v3-compatible speaker dictionary using Whisper and AudioProcessor.\n",
    "        \"\"\"\n",
    "        if not audio_bytes or not transcript:\n",
    "             print(\"Missing audio bytes or transcript in create_speaker_representation.\")\n",
    "             return None\n",
    "\n",
    "        # Whisper needs a file path, so save bytes to a temporary file\n",
    "        try:\n",
    "            with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=True) as tmp_audio_file:\n",
    "                tmp_audio_file.write(audio_bytes)\n",
    "                tmp_audio_file.flush() # Ensure data is written\n",
    "\n",
    "                # 1. Get word timings using Whisper\n",
    "                whisper_result = self.whisper_model.transcribe(tmp_audio_file.name, word_timestamps=True)\n",
    "                # Use the provided transcript for consistency, but Whisper timings\n",
    "                normalized_transcript = text_normalizations(transcript)\n",
    "\n",
    "                words_with_timings = []\n",
    "                if whisper_result and 'segments' in whisper_result:\n",
    "                    for segment in whisper_result['segments']:\n",
    "                        if 'words' in segment:\n",
    "                            for word_info in segment['words']:\n",
    "                                # Use original word casing/punctuation from Whisper's output if needed,\n",
    "                                # but strip excess whitespace for consistency.\n",
    "                                cleaned_word = word_info['word'].strip()\n",
    "                                if cleaned_word: # Ignore empty strings\n",
    "                                    words_with_timings.append({\n",
    "                                        'word': cleaned_word,\n",
    "                                        'start': float(word_info['start']),\n",
    "                                        'end': float(word_info['end'])\n",
    "                                    })\n",
    "                else:\n",
    "                    print(f\"Whisper did not return segments/words for: {transcript[:50]}...\")\n",
    "                    return None # Indicate failure\n",
    "\n",
    "                if not words_with_timings:\n",
    "                    print(f\"No word timings extracted by Whisper for: {transcript[:50]}...\")\n",
    "                    return None\n",
    "\n",
    "                # Prepare data dict for AudioProcessor\n",
    "                speaker_data_dict = {\n",
    "                    \"audio\": {\"bytes\": audio_bytes},\n",
    "                    \"text\": normalized_transcript, # Use the potentially normalized transcript\n",
    "                    \"words\": words_with_timings\n",
    "                }\n",
    "\n",
    "                # 2. Use AudioProcessor to create the speaker representation\n",
    "                v3_speaker = self.audio_processor.create_speaker_from_dict(speaker_data_dict)\n",
    "                return v3_speaker\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Error during speaker creation (Whisper/AudioProcessor): {e}\")\n",
    "            return None # Indicate failure\n",
    "\n",
    "\n",
    "    # --- V3 Changes: run method is now a generator ---\n",
    "    def process_dataset(self, dataset: Dataset):\n",
    "        \"\"\"\n",
    "        Processes a Hugging Face Dataset object in memory and yields training prompts.\n",
    "\n",
    "        Args:\n",
    "            dataset (Dataset): The Hugging Face dataset to process.\n",
    "                               Expected columns: 'text' (str) and 'audio' (dict with 'bytes').\n",
    "\n",
    "        Yields:\n",
    "            str: The processed training prompt string for each valid row.\n",
    "        \"\"\"\n",
    "        processed_count = 0\n",
    "        skipped_count = 0\n",
    "\n",
    "        # Iterate directly over the dataset\n",
    "        for i, item in enumerate(tqdm(dataset, desc=\"Processing Dataset\")):\n",
    "            try:\n",
    "                # --- Adapt to your dataset's column names ---\n",
    "                transcript = item.get('text')\n",
    "                audio_info = item.get('audio')\n",
    "                # --- End Adapt ---\n",
    "\n",
    "                if not transcript or not isinstance(transcript, str):\n",
    "                    print(f\"Row {i}: Skipping due to missing or invalid 'text' column.\")\n",
    "                    skipped_count += 1\n",
    "                    continue\n",
    "\n",
    "                audio_array = audio_info['array']\n",
    "                buffer = io.BytesIO()\n",
    "                # Ensure array is float32 for common compatibility, adjust subtype if needed\n",
    "                sf.write(buffer, audio_array.astype(np.float32), audio_info['sampling_rate'], format='WAV', subtype='FLOAT')\n",
    "                buffer.seek(0)\n",
    "                audio_bytes = buffer.getvalue()\n",
    "\n",
    "                # Create speaker representation\n",
    "                speaker = self.create_speaker_representation(audio_bytes, transcript)\n",
    "\n",
    "                if speaker is None:\n",
    "                    print(f\"Row {i}: Failed to create speaker representation for text: {transcript[:50]}... Skipping.\")\n",
    "                    skipped_count += 1\n",
    "                    continue\n",
    "\n",
    "                # Get the V3 training prompt\n",
    "                prompt = self.prompt_processor.get_training_prompt(speaker)\n",
    "\n",
    "                processed_count += 1\n",
    "                yield prompt # Yield the processed prompt string\n",
    "\n",
    "            except KeyboardInterrupt:\n",
    "                 print(\"Processing interrupted by user.\")\n",
    "                 break\n",
    "            except Exception as e:\n",
    "                print(f\"Row {i}: Unhandled error processing item: {e}\", exc_info=True)\n",
    "                skipped_count += 1\n",
    "                # Decide if you want to stop on errors or just skip\n",
    "                continue\n",
    "\n",
    "        print(f\"Dataset processing finished. Processed: {processed_count}, Skipped: {skipped_count}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "    _MODEL_TOKENIZER_PATH = \"OuteAI/Llama-OuteTTS-1.0-1B\"\n",
    "    _WHISPER_MODEL = \"turbo\" # Or \"small.en\", \"medium.en\", \"large-v2\", etc.\n",
    "\n",
    "\n",
    "    data_processor = DataCreationV3(\n",
    "        model_tokenizer_path=_MODEL_TOKENIZER_PATH,\n",
    "        whisper_model_name=_WHISPER_MODEL\n",
    "    )\n",
    "\n",
    "    # Process the dataset and collect prompts (or process iteratively)\n",
    "    all_prompts = []\n",
    "    print(\"Starting dataset processing...\")\n",
    "    procced_dataset = data_processor.process_dataset(dataset)\n",
    "    for prompt in procced_dataset:\n",
    "        if prompt:\n",
    "             all_prompts.append({'text': prompt})\n",
    "    dataset = Dataset.from_list(all_prompts)\n",
    "    print(\"Moving Whisper model to CPU\")\n",
    "    data_processor.whisper_model.to('cpu')\n",
    "    torch.cuda.empty_cache()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idAEIeSQ3xdS"
   },
   "source": [
    "<a name=\"Train\"></a>\n",
    "### Train the model\n",
    "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77,
     "referenced_widgets": [
      "37465088c410484e9fc136617fc939c8",
      "186cdf30cfb94ab29d043f2eeea628ec",
      "31637128f2e741ad93067d5cc295dc9f",
      "57575b5735eb4d2dbd0a89e925587964",
      "135f6dc595254051a21f9a3cb77366e0",
      "14e8d11447ea41aaa393e8ef3bdfec1c",
      "86876167afec4699aff52d32b2dbbc78",
      "3d891c6f331f411e81a7caf5341286e0",
      "42fc0a225a364a288c198835ffbcce92",
      "fb15eb3739d44a7bae430ccffa3ad65a",
      "40f1a38a3dea445795016161b087e92b"
     ]
    },
    "id": "95_Nn-89DhsL",
    "outputId": "dc50f5ca-73df-4f1b-d663-1aaf4d6fc36d"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Unsloth: Tokenizing [\"text\"] (num_proc=2):   0%|          | 0/1194 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "37465088c410484e9fc136617fc939c8"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "from trl import SFTConfig, SFTTrainer\n",
    "trainer = SFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    train_dataset = dataset,\n",
    "    dataset_text_field = \"text\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    packing = False, # Can make training 5x faster for short sequences.\n",
    "    args = SFTConfig(\n",
    "        per_device_train_batch_size = 2,\n",
    "        gradient_accumulation_steps = 4,\n",
    "        warmup_steps = 5,\n",
    "        # num_train_epochs = 1, # Set this for 1 full training run.\n",
    "        max_steps = 60,\n",
    "        learning_rate = 2e-4,\n",
    "        logging_steps = 1,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.001,\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 3407,\n",
    "        output_dir = \"outputs\",\n",
    "        report_to = \"none\", # Use TrackIO/WandB etc\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "c019e821-81a4-482d-8ab0-5414fd05159f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "GPU = Tesla T4. Max memory = 14.741 GB.\n",
      "7.215 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "# @title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "7821f3e3-13d5-4159-d091-e8a783cd44d3"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
      "   \\\\   /|    Num examples = 1,194 | Num Epochs = 1 | Total steps = 60\n",
      "O^O/ \\_/ \\    Batch size per device = 2 | Gradient accumulation steps = 4\n",
      "\\        /    Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8\n",
      " \"-____-\"     Trainable parameters = 13,631,488/1,262,028,800 (1.08% trained)\n",
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Unsloth: Will smartly offload gradients to save VRAM!\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [60/60 05:04, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.016100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.323200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.345600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.256300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.250400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.184200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.784700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.852700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.261300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.127400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>3.301400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.911300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>3.035000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>3.045200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>3.060300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>3.111800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2.770300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>3.150700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2.859400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2.887700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2.897900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>3.049500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2.632900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>2.920300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2.975600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>2.951000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>3.025400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>3.285500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>3.145400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>2.856300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>3.070600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>3.029900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>3.106500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>3.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2.811800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2.881100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>3.183000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2.899300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>3.048900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>3.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>2.872700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>3.022600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>3.030700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>2.990100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2.897100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2.948500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>3.045900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>3.148600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>3.142000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>3.014700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>2.887200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>2.531400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>2.902200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>2.983100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>2.799700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>3.013800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>3.036400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>3.150800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>3.013000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>2.968300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cellView": "form",
    "id": "pCqnaKmlO1U9",
    "outputId": "2448745d-d65d-451e-9f1a-d9ba03d69c9d",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "334.2293 seconds used for training.\n",
      "5.57 minutes used for training.\n",
      "Peak reserved memory = 7.215 GB.\n",
      "Peak reserved memory for training = 0.0 GB.\n",
      "Peak reserved memory % of max memory = 48.945 %.\n",
      "Peak reserved memory for training % of max memory = 0.0 %.\n"
     ]
    }
   ],
   "source": [
    "# @title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory / max_memory * 100, 3)\n",
    "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(\n",
    "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
    ")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ekOmTR1hSNcr"
   },
   "source": [
    "<a name=\"Inference\"></a>\n",
    "### Inference\n",
    "Let's run the model! You can change the prompts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "apUdB40Ep6Ki"
   },
   "outputs": [],
   "source": [
    "input_text = \"Hey there my name is Elise, and I'm a speech generation model that can sound like a person.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "id": "krYI8PrRJ6MX",
    "outputId": "b1162ee6-6f07-47cb-e2e0-09b603b39e77"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Generating token sequence...\n",
      "🔄 Using patched RepetitionPenaltyLogitsProcessor -> RepetitionPenaltyLogitsProcessorPatch | penalty_last_n: {penalty_last_n}\n",
      "Token sequence generated.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ],
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ]
     },
     "metadata": {}
    }
   ],
   "source": [
    "#@title Run Inference\n",
    "\n",
    "import torch\n",
    "import re\n",
    "import numpy as np\n",
    "from typing import Dict, Any\n",
    "import torchaudio.transforms as T\n",
    "from transformers import LogitsProcessor\n",
    "import transformers.generation.utils as generation_utils\n",
    "from transformers import AutoModelForCausalLM\n",
    "import re\n",
    "FastModel.for_inference(model)\n",
    "\n",
    "def get_audio(tokens):\n",
    "        decoded_output = tokenizer.batch_decode(tokens, skip_special_tokens=False)[0]\n",
    "        c1 = list(map(int,re.findall(r\"<\\|c1_(\\d+)\\|>\", decoded_output)))\n",
    "        c2 = list(map(int,re.findall(r\"<\\|c2_(\\d+)\\|>\", decoded_output)))\n",
    "\n",
    "        t = min(len(c1), len(c2))\n",
    "        c1 = c1[:t]\n",
    "        c2 = c2[:t]\n",
    "        output = [c1,c2]\n",
    "        if not output:\n",
    "            print(\"No audio tokens found in the output\")\n",
    "            return None\n",
    "\n",
    "        return data_processor.audio_processor.audio_codec.decode(\n",
    "            torch.tensor([output], dtype=torch.int64).to(data_processor.audio_processor.audio_codec.device)\n",
    "        )\n",
    "\n",
    "class RepetitionPenaltyLogitsProcessorPatch(LogitsProcessor):\n",
    "    def __init__(self, penalty: float):\n",
    "        penalty_last_n = 64\n",
    "        print(\"🔄 Using patched RepetitionPenaltyLogitsProcessor -> RepetitionPenaltyLogitsProcessorPatch | penalty_last_n: {penalty_last_n}\")\n",
    "        if penalty_last_n is not None:\n",
    "            if not isinstance(penalty_last_n, int) or penalty_last_n < 0:\n",
    "                raise ValueError(f\"`penalty_last_n` has to be a non-negative integer, but is {penalty_last_n}\")\n",
    "        if not isinstance(penalty, float) or penalty <= 0:\n",
    "            raise ValueError(f\"`penalty` has to be a positive float, but is {penalty}\")\n",
    "\n",
    "        self.penalty_last_n = penalty_last_n\n",
    "        self.penalty = penalty\n",
    "\n",
    "    @torch.no_grad()\n",
    "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            input_ids (`torch.LongTensor`):\n",
    "                Indices of input sequence tokens in the vocabulary (shape `(batch_size, sequence_length)`).\n",
    "            scores (`torch.FloatTensor`):\n",
    "                Prediction scores of a language modeling head (shape `(batch_size, vocab_size)`).\n",
    "\n",
    "        Returns:\n",
    "            `torch.FloatTensor`: The modified prediction scores.\n",
    "        \"\"\"\n",
    "        # Check if penalties should be applied\n",
    "        if self.penalty_last_n == 0 or self.penalty == 1.0:\n",
    "            return scores\n",
    "\n",
    "        batch_size, seq_len = input_ids.shape\n",
    "        vocab_size = scores.shape[-1]\n",
    "\n",
    "        # Process each batch item independently\n",
    "        for b in range(batch_size):\n",
    "            # 1. Determine the penalty window\n",
    "            start_index = max(0, seq_len - self.penalty_last_n)\n",
    "            window_indices = input_ids[b, start_index:] # Shape: (window_len,)\n",
    "\n",
    "            if window_indices.numel() == 0: # Skip if window is empty\n",
    "                continue\n",
    "\n",
    "            # 2. Find unique tokens within the window\n",
    "            tokens_in_window = set(window_indices.tolist())\n",
    "\n",
    "            # 3. Apply repetition penalty to the scores for this batch item\n",
    "            for token_id in tokens_in_window:\n",
    "                if token_id >= vocab_size:\n",
    "                    continue\n",
    "\n",
    "                logit = scores[b, token_id]\n",
    "\n",
    "                if logit <= 0:\n",
    "                    logit *= self.penalty\n",
    "                else:\n",
    "                    logit /= self.penalty\n",
    "\n",
    "                # Update the score\n",
    "                scores[b, token_id] = logit\n",
    "\n",
    "        return scores\n",
    "\n",
    "generation_utils.RepetitionPenaltyLogitsProcessor = RepetitionPenaltyLogitsProcessorPatch\n",
    "AutoModelForCausalLM.generate = generation_utils.GenerationMixin.generate\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    formated_text = \"<|text_start|>\"+input_text+\"<|text_end|>\"\n",
    "    prompt = \"\\n\".join([\n",
    "        \"<|im_start|>\",\n",
    "        formated_text,\n",
    "        \"<|audio_start|><|global_features_start|>\",\n",
    "    ])\n",
    "    with torch.inference_mode():\n",
    "        with torch.amp.autocast('cuda',dtype=model.dtype):\n",
    "          model_inputs = tokenizer([prompt], return_tensors=\"pt\").to(\"cuda\")\n",
    "\n",
    "          print(\"Generating token sequence...\")\n",
    "          generated_ids = model.generate(\n",
    "              **model_inputs,\n",
    "              temperature=0.4,\n",
    "              top_k=40,\n",
    "              top_p=0.9,\n",
    "              repetition_penalty=1.1,\n",
    "              min_p=0.05,\n",
    "              max_new_tokens=2048, # Limit generation length\n",
    "          )\n",
    "          print(\"Token sequence generated.\")\n",
    "\n",
    "\n",
    "    generated_ids_trimmed = generated_ids[:, model_inputs.input_ids.shape[1]:]\n",
    "    audio = get_audio(generated_ids)\n",
    "    audio = audio.cpu()\n",
    "    from IPython.display import Audio, display\n",
    "    display(Audio(audio.squeeze(0), rate=24000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMuVrWbjAzhc"
   },
   "source": [
    "<a name=\"Save\"></a>\n",
    "### Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
    "\n",
    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
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    },
    "id": "upcOlWe7A1vc",
    "outputId": "5727fb16-0597-4c50-c5be-284878cffc5a"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "('lora_model/tokenizer_config.json',\n",
       " 'lora_model/special_tokens_map.json',\n",
       " 'lora_model/tokenizer.json')"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "model.save_pretrained(\"lora_model\")  # Local saving\n",
    "tokenizer.save_pretrained(\"lora_model\")\n",
    "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
    "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f422JgM9sdVT"
   },
   "source": [
    "### Saving to float16\n",
    "\n",
    "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "iHjt_SMYsd3P"
   },
   "outputs": [],
   "source": [
    "# Merge to 16bit\n",
    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
    "\n",
    "# Merge to 4bit\n",
    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
    "\n",
    "# Just LoRA adapters\n",
    "if False:\n",
    "    model.save_pretrained(\"model\")\n",
    "    tokenizer.save_pretrained(\"model\")\n",
    "if False:\n",
    "    model.push_to_hub(\"hf/model\", token = \"\")\n",
    "    tokenizer.push_to_hub(\"hf/model\", token = \"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v1YjyrnPU0ST"
   },
   "source": [
    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
    "\n",
    "Some other links:\n",
    "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
    "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
    "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
    "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
    "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
    "\n",
    "  Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
    "</div>\n"
   ]
  }
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